Final Project Analisis Survival
Kelompok 1
2024-12-14
Regresi Survival Untuk Analisis Ketahanan Hidup Pasien Penyakit Sirosis
Anggota Kelompok 1:
1. Saeful Fikri - 5003211049
2. Hanif Choiruddin - 5003211063
3. I Wayan Rama - 5003211112
Load Data
library(dplyr)## Warning: package 'dplyr' was built under R version 4.2.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)## Warning: package 'ggplot2' was built under R version 4.2.3library(gridExtra)## Warning: package 'gridExtra' was built under R version 4.2.2##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinelibrary(writexl)## Warning: package 'writexl' was built under R version 4.2.3library(survival)## Warning: package 'survival' was built under R version 4.2.3library(readxl)setwd("C:/KULIAH/Semester 7/Ansur E/FP ANSUR")
data = read.csv("cirrhosis.csv")
head(data)## ID N_Days Status Drug Age Sex Ascites Hepatomegaly Spiders Edema
## 1 1 400 D D-penicillamine 21464 F Y Y Y Y
## 2 2 4500 C D-penicillamine 20617 F N Y Y N
## 3 3 1012 D D-penicillamine 25594 M N N N S
## 4 4 1925 D D-penicillamine 19994 F N Y Y S
## 5 5 1504 CL Placebo 13918 F N Y Y N
## 6 6 2503 D Placebo 24201 F N Y N N
## Bilirubin Cholesterol Albumin Copper Alk_Phos SGOT Tryglicerides Platelets
## 1 14.5 261 2.60 156 1718.0 137.95 172 190
## 2 1.1 302 4.14 54 7394.8 113.52 88 221
## 3 1.4 176 3.48 210 516.0 96.10 55 151
## 4 1.8 244 2.54 64 6121.8 60.63 92 183
## 5 3.4 279 3.53 143 671.0 113.15 72 136
## 6 0.8 248 3.98 50 944.0 93.00 63 NA
## Prothrombin Stage
## 1 12.2 4
## 2 10.6 3
## 3 12.0 4
## 4 10.3 4
## 5 10.9 3
## 6 11.0 3Preprocessing dan Eksplorasi Data
str(data)## 'data.frame': 418 obs. of 20 variables:
## $ ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ N_Days : int 400 4500 1012 1925 1504 2503 1832 2466 2400 51 ...
## $ Status : chr "D" "C" "D" "D" ...
## $ Drug : chr "D-penicillamine" "D-penicillamine" "D-penicillamine" "D-penicillamine" ...
## $ Age : int 21464 20617 25594 19994 13918 24201 20284 19379 15526 25772 ...
## $ Sex : chr "F" "F" "M" "F" ...
## $ Ascites : chr "Y" "N" "N" "N" ...
## $ Hepatomegaly : chr "Y" "Y" "N" "Y" ...
## $ Spiders : chr "Y" "Y" "N" "Y" ...
## $ Edema : chr "Y" "N" "S" "S" ...
## $ Bilirubin : num 14.5 1.1 1.4 1.8 3.4 0.8 1 0.3 3.2 12.6 ...
## $ Cholesterol : int 261 302 176 244 279 248 322 280 562 200 ...
## $ Albumin : num 2.6 4.14 3.48 2.54 3.53 3.98 4.09 4 3.08 2.74 ...
## $ Copper : int 156 54 210 64 143 50 52 52 79 140 ...
## $ Alk_Phos : num 1718 7395 516 6122 671 ...
## $ SGOT : num 137.9 113.5 96.1 60.6 113.2 ...
## $ Tryglicerides: int 172 88 55 92 72 63 213 189 88 143 ...
## $ Platelets : int 190 221 151 183 136 NA 204 373 251 302 ...
## $ Prothrombin : num 12.2 10.6 12 10.3 10.9 11 9.7 11 11 11.5 ...
## $ Stage : int 4 3 4 4 3 3 3 3 2 4 ...# Jumlah data NA
sum(is.na(data))## [1] 1033colSums(is.na(data))## ID N_Days Status Drug Age
## 0 0 0 106 0
## Sex Ascites Hepatomegaly Spiders Edema
## 0 106 106 106 0
## Bilirubin Cholesterol Albumin Copper Alk_Phos
## 0 134 0 108 106
## SGOT Tryglicerides Platelets Prothrombin Stage
## 106 136 11 2 6# Jumlah data Duplikat
duplicates <- duplicated(data)
sum(duplicates)## [1] 0# Ganti tipe data Stage (int --> factor)
data$Stage <- as.factor(data$Stage)
# Ganti semua variabel bertipe data chr menjadi factor
data <- data %>%
mutate(across(where(is.character), as.factor))
# Hapus baris data yang NA di variabel Drug
data <- na.omit(data, col = "Drug")
str(data)## 'data.frame': 276 obs. of 20 variables:
## $ ID : int 1 2 3 4 5 7 8 9 10 11 ...
## $ N_Days : int 400 4500 1012 1925 1504 1832 2466 2400 51 3762 ...
## $ Status : Factor w/ 3 levels "C","CL","D": 3 1 3 3 2 1 3 3 3 3 ...
## $ Drug : Factor w/ 2 levels "D-penicillamine",..: 1 1 1 1 2 2 2 1 2 2 ...
## $ Age : int 21464 20617 25594 19994 13918 20284 19379 15526 25772 19619 ...
## $ Sex : Factor w/ 2 levels "F","M": 1 1 2 1 1 1 1 1 1 1 ...
## $ Ascites : Factor w/ 2 levels "N","Y": 2 1 1 1 1 1 1 1 2 1 ...
## $ Hepatomegaly : Factor w/ 2 levels "N","Y": 2 2 1 2 2 2 1 1 1 2 ...
## $ Spiders : Factor w/ 2 levels "N","Y": 2 2 1 2 2 1 1 2 2 2 ...
## $ Edema : Factor w/ 3 levels "N","S","Y": 3 1 2 2 1 1 1 1 3 1 ...
## $ Bilirubin : num 14.5 1.1 1.4 1.8 3.4 1 0.3 3.2 12.6 1.4 ...
## $ Cholesterol : int 261 302 176 244 279 322 280 562 200 259 ...
## $ Albumin : num 2.6 4.14 3.48 2.54 3.53 4.09 4 3.08 2.74 4.16 ...
## $ Copper : int 156 54 210 64 143 52 52 79 140 46 ...
## $ Alk_Phos : num 1718 7395 516 6122 671 ...
## $ SGOT : num 137.9 113.5 96.1 60.6 113.2 ...
## $ Tryglicerides: int 172 88 55 92 72 213 189 88 143 79 ...
## $ Platelets : int 190 221 151 183 136 204 373 251 302 258 ...
## $ Prothrombin : num 12.2 10.6 12 10.3 10.9 9.7 11 11 11.5 12 ...
## $ Stage : Factor w/ 4 levels "1","2","3","4": 4 3 4 4 3 3 3 2 4 4 ...
## - attr(*, "na.action")= 'omit' Named int [1:142] 6 14 40 41 42 45 49 53 58 70 ...
## ..- attr(*, "names")= chr [1:142] "6" "14" "40" "41" ...#Kategorisasi Variabel Numerik
data$Bilirubin_cat = cut(data$Bilirubin,
breaks = c(0, 1.2, Inf), # Kategori: 0-1.2, >1.2
labels = c("Normal", "Increased"),
right = FALSE)
data$Cholesterol_cat = cut(data$Cholesterol,
breaks = c(0, 200, 239, Inf), # Kategori: <200, 200-239, >239
labels = c("Normal", "Borderline", "High"),
right = FALSE)
data$Platelets_cat = cut(data$Platelets,
breaks = c(0, 150, 450, Inf), # Kategori: <150, 150-450, >450
labels = c("Low", "Normal", "High"),
right = FALSE)
data$Prothrombin_cat = cut(data$Prothrombin,
breaks = c(0, 12, Inf), # Kategori: 0-12, >12
labels = c("Normal", "Prolonged"),
right = FALSE)
data$Alk_Phos_cat = cut(data$Alk_Phos,
breaks = c(0, 1300, Inf), # Kategori: 0-1300, >1300
labels = c("Normal", "High"),
right = FALSE)
data$SGOT_cat = cut(data$SGOT,
breaks = c(0, 48, Inf), # Kategori: 0-48, >48
labels = c("Normal", "High"),
right = FALSE)
data$Age_years = data$Age / 365.25
data$Age_cat = cut(data$Age_years,
breaks = c(-Inf, 40, 60, Inf), # Kategori: <40, 40-60, >60
labels = c("Young", "Middle-aged", "Old"),
include.lowest = TRUE)
str(data)## 'data.frame': 276 obs. of 28 variables:
## $ ID : int 1 2 3 4 5 7 8 9 10 11 ...
## $ N_Days : int 400 4500 1012 1925 1504 1832 2466 2400 51 3762 ...
## $ Status : Factor w/ 3 levels "C","CL","D": 3 1 3 3 2 1 3 3 3 3 ...
## $ Drug : Factor w/ 2 levels "D-penicillamine",..: 1 1 1 1 2 2 2 1 2 2 ...
## $ Age : int 21464 20617 25594 19994 13918 20284 19379 15526 25772 19619 ...
## $ Sex : Factor w/ 2 levels "F","M": 1 1 2 1 1 1 1 1 1 1 ...
## $ Ascites : Factor w/ 2 levels "N","Y": 2 1 1 1 1 1 1 1 2 1 ...
## $ Hepatomegaly : Factor w/ 2 levels "N","Y": 2 2 1 2 2 2 1 1 1 2 ...
## $ Spiders : Factor w/ 2 levels "N","Y": 2 2 1 2 2 1 1 2 2 2 ...
## $ Edema : Factor w/ 3 levels "N","S","Y": 3 1 2 2 1 1 1 1 3 1 ...
## $ Bilirubin : num 14.5 1.1 1.4 1.8 3.4 1 0.3 3.2 12.6 1.4 ...
## $ Cholesterol : int 261 302 176 244 279 322 280 562 200 259 ...
## $ Albumin : num 2.6 4.14 3.48 2.54 3.53 4.09 4 3.08 2.74 4.16 ...
## $ Copper : int 156 54 210 64 143 52 52 79 140 46 ...
## $ Alk_Phos : num 1718 7395 516 6122 671 ...
## $ SGOT : num 137.9 113.5 96.1 60.6 113.2 ...
## $ Tryglicerides : int 172 88 55 92 72 213 189 88 143 79 ...
## $ Platelets : int 190 221 151 183 136 204 373 251 302 258 ...
## $ Prothrombin : num 12.2 10.6 12 10.3 10.9 9.7 11 11 11.5 12 ...
## $ Stage : Factor w/ 4 levels "1","2","3","4": 4 3 4 4 3 3 3 2 4 4 ...
## $ Bilirubin_cat : Factor w/ 2 levels "Normal","Increased": 2 1 2 2 2 1 1 2 2 2 ...
## $ Cholesterol_cat: Factor w/ 3 levels "Normal","Borderline",..: 3 3 1 3 3 3 3 3 2 3 ...
## $ Platelets_cat : Factor w/ 3 levels "Low","Normal",..: 2 2 2 2 1 2 2 2 2 2 ...
## $ Prothrombin_cat: Factor w/ 2 levels "Normal","Prolonged": 2 1 2 1 1 1 1 1 1 2 ...
## $ Alk_Phos_cat : Factor w/ 2 levels "Normal","High": 2 2 1 2 1 1 2 2 1 1 ...
## $ SGOT_cat : Factor w/ 2 levels "Normal","High": 2 2 2 2 2 2 1 2 2 2 ...
## $ Age_years : num 58.8 56.4 70.1 54.7 38.1 ...
## $ Age_cat : Factor w/ 3 levels "Young","Middle-aged",..: 2 2 3 2 1 2 2 2 3 2 ...
## - attr(*, "na.action")= 'omit' Named int [1:142] 6 14 40 41 42 45 49 53 58 70 ...
## ..- attr(*, "names")= chr [1:142] "6" "14" "40" "41" ...# Boxplot
boxplot(data$N_Days, main = "Boxplot of N_Days")
# Seleksi variabel terpilih
selected_vars = c("ID", "N_Days", "Status", "Drug", "Sex", "Ascites", "Spiders", "Edema",
"Stage", "Bilirubin_cat", "Cholesterol_cat", "Platelets_cat",
"Prothrombin_cat", "Alk_Phos_cat", "SGOT_cat")
df <- data[, selected_vars]
head(df)## ID N_Days Status Drug Sex Ascites Spiders Edema Stage
## 1 1 400 D D-penicillamine F Y Y Y 4
## 2 2 4500 C D-penicillamine F N Y N 3
## 3 3 1012 D D-penicillamine M N N S 4
## 4 4 1925 D D-penicillamine F N Y S 4
## 5 5 1504 CL Placebo F N Y N 3
## 7 7 1832 C Placebo F N N N 3
## Bilirubin_cat Cholesterol_cat Platelets_cat Prothrombin_cat Alk_Phos_cat
## 1 Increased High Normal Prolonged High
## 2 Normal High Normal Normal High
## 3 Increased Normal Normal Prolonged Normal
## 4 Increased High Normal Normal High
## 5 Increased High Low Normal Normal
## 7 Normal High Normal Normal Normal
## SGOT_cat
## 1 High
## 2 High
## 3 High
## 4 High
## 5 High
## 7 High# Statistika deskriptif
summary(df$N_Days)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 41 1186 1788 1979 2690 4556# Filter kolom bertipe data kategorik
cat_col <- df[sapply(df, is.factor)]
# Buat pie chart tiap variabel kategorik
plot_list <- lapply(colnames(cat_col), function(col) {
counts <- table(cat_col[[col]])
df <- data.frame(Category = names(counts), Freq = as.numeric(counts))
df$Percentage <- round(df$Freq / sum(df$Freq) * 100, 1)
df$Label <- ifelse(df$Percentage == max(df$Percentage), paste(df$Percentage, "%"), "")
ggplot(df, aes(x = "", y = Freq, fill = Category)) +
geom_bar(stat = "identity", width = 1) +
coord_polar(theta = "y") +
theme_void() +
ggtitle(col) +
theme(plot.title = element_text(hjust = 0.5, size = 14, face = "bold")) +
theme(legend.title = element_blank(), legend.position = "top") +
geom_text(aes(label = Label), color = "white", size = 5,
fontface = "bold", position = position_stack(vjust = 0.5))
})
# Menampilkan semua pie chart dalam satu grid
grid.arrange(grobs = plot_list, ncol = 5)
# Simpan data dalam bentuk xlsx
write_xlsx(df, "datafix.xlsx")data_fp = read_excel("data_fp.xlsx")
head(data_fp)## # A tibble: 6 × 20
## ID N_Days Status Drug Age Sex Ascites Hepatomegaly Spiders Edema
## <dbl> <dbl> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
## 1 1 400 D D-penicill… 21464 F Y Y Y Y
## 2 2 4500 C D-penicill… 20617 F N Y Y N
## 3 3 1012 D D-penicill… 25594 M N N N S
## 4 4 1925 D D-penicill… 19994 F N Y Y S
## 5 5 1504 C Placebo 13918 F N Y Y N
## 6 7 1832 C Placebo 20284 F N Y N N
## # ℹ 10 more variables: Bilirubin <dbl>, Cholesterol <dbl>, Albumin <dbl>,
## # Copper <dbl>, Alk_Phos <dbl>, SGOT <dbl>, Tryglicerides <dbl>,
## # Platelets <dbl>, Prothrombin <dbl>, Stage <chr>#pilih variabel
select_var = c('N_Days','Status','Drug','Age', 'Sex', 'Ascites', 'Spiders', 'Bilirubin', 'Cholesterol', 'Alk_Phos', 'SGOT', 'Platelets', 'Prothrombin', 'Stage')
df = data_fp[select_var]
#kategori -> faktor
df$Drug = as.factor(df$Drug)
df$Sex = as.factor(df$Sex)
df$Ascites = as.factor(df$Ascites)
df$Spiders = as.factor(df$Spiders)
df$Stage = as.factor(df$Stage)
df$Status = as.factor(df$Status)
#ubah satuan age menjadi tahun
df$Age_years = df$Age / 365.25
#numerik -> kategorik
numerical_vars = c("Bilirubin", "Cholesterol", "Alk_Phos", "SGOT", "Platelets", "Prothrombin")
df$Bilirubin_cat = cut(df$Bilirubin,
breaks = c(0, 1.2, Inf),
labels = c("Normal", "Increased"),
right = FALSE)
df$Cholesterol_cat = cut(df$Cholesterol,
breaks = c(0, 200, 239, Inf),
labels = c("Normal", "Borderline", "High"),
right = FALSE)
df$Platelets_cat = cut(df$Platelets,
breaks = c(0, 150, 450, Inf),
labels = c("Low", "Normal", "High"),
right = FALSE)
df$Prothrombin_cat = cut(df$Prothrombin,
breaks = c(0, 12, Inf),
labels = c("Normal", "Prolonged"),
right = FALSE)
df$Alk_Phos_cat = cut(df$Alk_Phos,
breaks = c(0, 1300, Inf),
labels = c("Normal", "High"),
right = FALSE)
df$SGOT_cat = cut(df$SGOT,
breaks = c(0, 48, Inf),
labels = c("Normal", "High"),
right = FALSE)
df$Age_cat = cut(
df$Age_years,
breaks = c(-Inf, 40, 60, Inf), # Kategori: <40, 40-60, >60
labels = c("Young", "Middle-aged", "Old"),
include.lowest = TRUE
)
str(df)## tibble [276 × 22] (S3: tbl_df/tbl/data.frame)
## $ N_Days : num [1:276] 400 4500 1012 1925 1504 ...
## $ Status : Factor w/ 2 levels "C","D": 2 1 2 2 1 1 2 2 2 2 ...
## $ Drug : Factor w/ 2 levels "D-penicillamine",..: 1 1 1 1 2 2 2 1 2 2 ...
## $ Age : num [1:276] 21464 20617 25594 19994 13918 ...
## $ Sex : Factor w/ 2 levels "F","M": 1 1 2 1 1 1 1 1 1 1 ...
## $ Ascites : Factor w/ 2 levels "N","Y": 2 1 1 1 1 1 1 1 2 1 ...
## $ Spiders : Factor w/ 2 levels "N","Y": 2 2 1 2 2 1 1 2 2 2 ...
## $ Bilirubin : num [1:276] 14.5 1.1 1.4 1.8 3.4 1 0.3 3.2 12.6 1.4 ...
## $ Cholesterol : num [1:276] 261 302 176 244 279 322 280 562 200 259 ...
## $ Alk_Phos : num [1:276] 1718 7395 516 6122 671 ...
## $ SGOT : num [1:276] 137.9 113.5 96.1 60.6 113.2 ...
## $ Platelets : num [1:276] 190 221 151 183 136 204 373 251 302 258 ...
## $ Prothrombin : num [1:276] 12.2 10.6 12 10.3 10.9 9.7 11 11 11.5 12 ...
## $ Stage : Factor w/ 4 levels "1","2","3","4": 4 3 4 4 3 3 3 2 4 4 ...
## $ Age_years : num [1:276] 58.8 56.4 70.1 54.7 38.1 ...
## $ Bilirubin_cat : Factor w/ 2 levels "Normal","Increased": 2 1 2 2 2 1 1 2 2 2 ...
## $ Cholesterol_cat: Factor w/ 3 levels "Normal","Borderline",..: 3 3 1 3 3 3 3 3 2 3 ...
## $ Platelets_cat : Factor w/ 3 levels "Low","Normal",..: 2 2 2 2 1 2 2 2 2 2 ...
## $ Prothrombin_cat: Factor w/ 2 levels "Normal","Prolonged": 2 1 2 1 1 1 1 1 1 2 ...
## $ Alk_Phos_cat : Factor w/ 2 levels "Normal","High": 2 2 1 2 1 1 2 2 1 1 ...
## $ SGOT_cat : Factor w/ 2 levels "Normal","High": 2 2 2 2 2 2 1 2 2 2 ...
## $ Age_cat : Factor w/ 3 levels "Young","Middle-aged",..: 2 2 3 2 1 2 2 2 3 2 ...K-M Curve
# Variabel outcome (waktu survival)
y = Surv(time = df$N_Days, event = df$Status == "D")
y## [1] 400 4500+ 1012 1925 1504+ 1832+ 2466 2400 51 3762 304 3577+
## [13] 3584 3672+ 769 131 4232+ 1356 3445+ 673 264 4079 4127+ 1444
## [25] 77 549 4509+ 321 3839 4523+ 3170 3933+ 2847 3611+ 223 3244
## [37] 2297 4556+ 3428 2256 2576+ 4427+ 2598 3853 2386 1434 1360 1847
## [49] 3282 2224 4365+ 4256+ 3090 859 1487 3992+ 4191 2769 4039+ 1170
## [61] 4196+ 4184+ 4190+ 1827 1191 71 326 1690 3707+ 890 2540 3574
## [73] 4050+ 4032+ 3358 1657 198 2452+ 1741 2689 460 388 3913+ 750
## [85] 611 3823+ 3820+ 552 3581+ 3099+ 110 3086 3092+ 3388+ 2583 2504+
## [97] 2105 2350+ 3445 980 3395 3422+ 3336+ 1083 2288 515 2033+ 191
## [109] 3297+ 3069+ 2468+ 3255+ 1413 850 2944+ 2796 3149+ 3150+ 3098+ 2990+
## [121] 1297 2106+ 3059+ 3050+ 2419 786 943 2976+ 2995+ 1427 762 2870+
## [133] 1152 2863+ 140 2666+ 853 2835+ 2475+ 1536 2772+ 2797+ 186 2055
## [145] 1077 2721+ 1682 1212 2692+ 2301+ 2657+ 2624+ 2609+ 2573+ 2563+ 2556+
## [157] 2241+ 974 2527+ 1576 733 2332+ 2456+ 216 2443+ 797 2449+ 2330+
## [169] 2363+ 2365+ 2357+ 1592+ 2318+ 2294+ 2272+ 2221+ 2090 2255+ 904 2216+
## [181] 2224+ 2176+ 2178+ 1786 1080 790 2157+ 1235 2050+ 597 334 1945+
## [193] 2022+ 1978+ 999 1967+ 348 1979+ 1165 1951+ 1932+ 1776+ 1882+ 1908+
## [205] 1882+ 694 1831+ 837+ 1810+ 930 1690 1790+ 1435+ 732+ 1785+ 1783+
## [217] 1769+ 1457+ 1770+ 1765+ 737+ 1735+ 1701+ 1614+ 1702+ 1615+ 1656+ 1666+
## [229] 1301+ 1542+ 1084+ 1614+ 179 1191 1363+ 1568+ 1569+ 1525+ 1558+ 1349+
## [241] 1481+ 1434+ 1420+ 1433+ 1412+ 41 1455+ 1030+ 1418+ 1401+ 1408+ 1234+
## [253] 1067+ 799 1363+ 901+ 1329+ 1320+ 1302+ 877+ 1321+ 533+ 1300+ 1293+
## [265] 1295+ 1271+ 1250+ 1230+ 1216+ 1216+ 1149+ 1153+ 994+ 939+ 839+ 788+# PLOT K-M CURVE
variables = c("Drug", "Sex", "Ascites", "Spiders",
"Bilirubin_cat", "Cholesterol_cat", "Alk_Phos_cat",
"SGOT_cat", "Platelets_cat", "Prothrombin_cat", "Age_cat")
for (var in variables) {
df_clean = df[!is.na(df[[var]]), ]
fit = survfit(y ~ df_clean[[var]], data = df_clean)
plot(fit,
main = paste("KM Curve by", var),
xlab = "Time (Days)",
ylab = "Survival Probability",
col = 1:length(unique(df_clean[[var]])),
lty = 1,
conf.int = FALSE)
legend("topright", cex=0.6,
legend = levels(df_clean[[var]]),
col = 1:length(unique(df_clean[[var]])),
lty = 1,
title = var)
}
LOG-RANK
# LOG-RANK
variables = c("Drug", "Sex", "Ascites", "Spiders",
"Bilirubin_cat", "Cholesterol_cat", "Alk_Phos_cat",
"SGOT_cat", "Platelets_cat", "Prothrombin_cat", "Age_cat")
for (var in variables) {
df_clean = df[!is.na(df[[var]]), ]
log_rank_test = survdiff(y ~ df_clean[[var]], data = df_clean)
cat("\nLog-Rank Test for", var, ":\n")
print(log_rank_test)
cat("\n-----------------------------------------------\n")
}##
## Log-Rank Test for Drug :
## Call:
## survdiff(formula = y ~ df_clean[[var]], data = df_clean)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## df_clean[[var]]=D-penicillamine 136 57 53.7 0.209 0.405
## df_clean[[var]]=Placebo 140 54 57.3 0.195 0.405
##
## Chisq= 0.4 on 1 degrees of freedom, p= 0.5
##
## -----------------------------------------------
##
## Log-Rank Test for Sex :
## Call:
## survdiff(formula = y ~ df_clean[[var]], data = df_clean)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## df_clean[[var]]=F 242 90 97.3 0.546 4.47
## df_clean[[var]]=M 34 21 13.7 3.878 4.47
##
## Chisq= 4.5 on 1 degrees of freedom, p= 0.03
##
## -----------------------------------------------
##
## Log-Rank Test for Ascites :
## Call:
## survdiff(formula = y ~ df_clean[[var]], data = df_clean)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## df_clean[[var]]=N 257 93 108.69 2.26 110
## df_clean[[var]]=Y 19 18 2.31 106.56 110
##
## Chisq= 110 on 1 degrees of freedom, p= <2e-16
##
## -----------------------------------------------
##
## Log-Rank Test for Spiders :
## Call:
## survdiff(formula = y ~ df_clean[[var]], data = df_clean)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## df_clean[[var]]=N 196 65 87.3 5.69 27.2
## df_clean[[var]]=Y 80 46 23.7 20.96 27.2
##
## Chisq= 27.2 on 1 degrees of freedom, p= 2e-07
##
## -----------------------------------------------
##
## Log-Rank Test for Bilirubin_cat :
## Call:
## survdiff(formula = y ~ df_clean[[var]], data = df_clean)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## df_clean[[var]]=Normal 115 19 58.2 26.4 56.4
## df_clean[[var]]=Increased 161 92 52.8 29.0 56.4
##
## Chisq= 56.4 on 1 degrees of freedom, p= 6e-14
##
## -----------------------------------------------
##
## Log-Rank Test for Cholesterol_cat :
## Call:
## survdiff(formula = y ~ df_clean[[var]], data = df_clean)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## df_clean[[var]]=Normal 20 11 6.18 3.761079 3.99579
## df_clean[[var]]=Borderline 36 10 14.72 1.512593 1.74892
## df_clean[[var]]=High 220 90 90.10 0.000117 0.00062
##
## Chisq= 5.3 on 2 degrees of freedom, p= 0.07
##
## -----------------------------------------------
##
## Log-Rank Test for Alk_Phos_cat :
## Call:
## survdiff(formula = y ~ df_clean[[var]], data = df_clean)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## df_clean[[var]]=Normal 140 40 55.4 4.28 8.64
## df_clean[[var]]=High 136 71 55.6 4.26 8.64
##
## Chisq= 8.6 on 1 degrees of freedom, p= 0.003
##
## -----------------------------------------------
##
## Log-Rank Test for SGOT_cat :
## Call:
## survdiff(formula = y ~ df_clean[[var]], data = df_clean)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## df_clean[[var]]=Normal 6 3 2.42 0.13794 0.142
## df_clean[[var]]=High 270 108 108.58 0.00308 0.142
##
## Chisq= 0.1 on 1 degrees of freedom, p= 0.7
##
## -----------------------------------------------
##
## Log-Rank Test for Platelets_cat :
## Call:
## survdiff(formula = y ~ df_clean[[var]], data = df_clean)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## df_clean[[var]]=Low 32 20 10.08 9.7661 10.7681
## df_clean[[var]]=Normal 236 88 97.37 0.9026 7.3666
## df_clean[[var]]=High 8 3 3.55 0.0841 0.0875
##
## Chisq= 10.8 on 2 degrees of freedom, p= 0.005
##
## -----------------------------------------------
##
## Log-Rank Test for Prothrombin_cat :
## Call:
## survdiff(formula = y ~ df_clean[[var]], data = df_clean)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## df_clean[[var]]=Normal 245 85 102.32 2.93 37.7
## df_clean[[var]]=Prolonged 31 26 8.68 34.58 37.7
##
## Chisq= 37.7 on 1 degrees of freedom, p= 8e-10
##
## -----------------------------------------------
##
## Log-Rank Test for Age_cat :
## Call:
## survdiff(formula = y ~ df_clean[[var]], data = df_clean)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## df_clean[[var]]=Young 53 12 20.0 3.220 3.96
## df_clean[[var]]=Middle-aged 174 69 75.5 0.556 1.76
## df_clean[[var]]=Old 49 30 15.5 13.588 15.89
##
## Chisq= 17.4 on 2 degrees of freedom, p= 2e-04
##
## -----------------------------------------------COX PH Model
cox_model = coxph(y ~ Drug + Age + Sex + Ascites + Spiders + Bilirubin + Cholesterol + Alk_Phos + SGOT + Platelets + Prothrombin + Stage, data = df)
summary(cox_model)## Call:
## coxph(formula = y ~ Drug + Age + Sex + Ascites + Spiders + Bilirubin +
## Cholesterol + Alk_Phos + SGOT + Platelets + Prothrombin +
## Stage, data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DrugPlacebo -1.501e-01 8.606e-01 2.039e-01 -0.736 0.46166
## Age 8.581e-05 1.000e+00 3.014e-05 2.847 0.00442 **
## SexM 2.285e-01 1.257e+00 2.802e-01 0.816 0.41472
## AscitesY 6.452e-01 1.906e+00 3.412e-01 1.891 0.05866 .
## SpidersY 2.479e-01 1.281e+00 2.315e-01 1.071 0.28432
## Bilirubin 9.643e-02 1.101e+00 2.112e-02 4.565 4.99e-06 ***
## Cholesterol 1.869e-04 1.000e+00 4.534e-04 0.412 0.68024
## Alk_Phos 5.694e-05 1.000e+00 3.839e-05 1.483 0.13807
## SGOT 5.362e-03 1.005e+00 1.901e-03 2.821 0.00479 **
## Platelets -3.987e-04 9.996e-01 1.125e-03 -0.354 0.72297
## Prothrombin 2.780e-01 1.321e+00 1.126e-01 2.469 0.01354 *
## Stage2 1.424e+00 4.152e+00 1.094e+00 1.301 0.19329
## Stage3 1.809e+00 6.103e+00 1.060e+00 1.706 0.08802 .
## Stage4 2.342e+00 1.040e+01 1.051e+00 2.228 0.02587 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## DrugPlacebo 0.8606 1.16198 0.5770 1.283
## Age 1.0001 0.99991 1.0000 1.000
## SexM 1.2567 0.79571 0.7257 2.176
## AscitesY 1.9064 0.52456 0.9767 3.721
## SpidersY 1.2813 0.78045 0.8139 2.017
## Bilirubin 1.1012 0.90808 1.0566 1.148
## Cholesterol 1.0002 0.99981 0.9993 1.001
## Alk_Phos 1.0001 0.99994 1.0000 1.000
## SGOT 1.0054 0.99465 1.0016 1.009
## Platelets 0.9996 1.00040 0.9974 1.002
## Prothrombin 1.3205 0.75728 1.0590 1.647
## Stage2 4.1520 0.24085 0.4862 35.459
## Stage3 6.1029 0.16386 0.7639 48.759
## Stage4 10.4033 0.09612 1.3256 81.645
##
## Concordance= 0.839 (se = 0.018 )
## Likelihood ratio test= 148 on 14 df, p=<2e-16
## Wald test = 161.4 on 14 df, p=<2e-16
## Score (logrank) test = 256.8 on 14 df, p=<2e-16cox_num = coxph(y ~ Age + Bilirubin + Cholesterol + Alk_Phos + SGOT + Platelets + Prothrombin, data = df)
summary(cox_num)## Call:
## coxph(formula = y ~ Age + Bilirubin + Cholesterol + Alk_Phos +
## SGOT + Platelets + Prothrombin, data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Age 1.276e-04 1.000e+00 2.887e-05 4.422 9.80e-06 ***
## Bilirubin 1.137e-01 1.120e+00 1.654e-02 6.874 6.23e-12 ***
## Cholesterol 2.169e-04 1.000e+00 4.299e-04 0.504 0.613958
## Alk_Phos 4.558e-05 1.000e+00 3.646e-05 1.250 0.211275
## SGOT 4.360e-03 1.004e+00 1.792e-03 2.433 0.014995 *
## Platelets -1.717e-03 9.983e-01 1.099e-03 -1.563 0.118052
## Prothrombin 2.694e-01 1.309e+00 7.311e-02 3.685 0.000229 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Age 1.0001 0.9999 1.0001 1.000
## Bilirubin 1.1204 0.8925 1.0847 1.157
## Cholesterol 1.0002 0.9998 0.9994 1.001
## Alk_Phos 1.0000 1.0000 1.0000 1.000
## SGOT 1.0044 0.9956 1.0008 1.008
## Platelets 0.9983 1.0017 0.9961 1.000
## Prothrombin 1.3092 0.7638 1.1344 1.511
##
## Concordance= 0.808 (se = 0.021 )
## Likelihood ratio test= 115.2 on 7 df, p=<2e-16
## Wald test = 138.1 on 7 df, p=<2e-16
## Score (logrank) test = 201.1 on 7 df, p=<2e-16cox_cat = coxph(y ~ Drug + Sex + Ascites + Spiders + Stage, data = df)
summary(cox_cat)## Call:
## coxph(formula = y ~ Drug + Sex + Ascites + Spiders + Stage, data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DrugPlacebo -0.07599 0.92682 0.19651 -0.387 0.69897
## SexM 0.47497 1.60797 0.24699 1.923 0.05448 .
## AscitesY 1.63670 5.13819 0.30080 5.441 5.29e-08 ***
## SpidersY 0.59587 1.81462 0.20965 2.842 0.00448 **
## Stage2 1.35801 3.88846 1.03650 1.310 0.19013
## Stage3 1.87686 6.53298 1.01567 1.848 0.06461 .
## Stage4 2.33090 10.28723 1.02160 2.282 0.02251 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## DrugPlacebo 0.9268 1.07896 0.6306 1.362
## SexM 1.6080 0.62190 0.9909 2.609
## AscitesY 5.1382 0.19462 2.8495 9.265
## SpidersY 1.8146 0.55108 1.2032 2.737
## Stage2 3.8885 0.25717 0.5099 29.652
## Stage3 6.5330 0.15307 0.8924 47.824
## Stage4 10.2872 0.09721 1.3890 76.188
##
## Concordance= 0.758 (se = 0.025 )
## Likelihood ratio test= 81.93 on 7 df, p=6e-15
## Wald test = 100.5 on 7 df, p=<2e-16
## Score (logrank) test = 145.4 on 7 df, p=<2e-16Cek Asumsi PH
Transformasi Log-log (Log-log Plot)
#fugnsi transformasi minus log-log
minusloglog = function(surv) {
-log(-log(surv))
}
results_list = list()
variables = c("Drug", "Sex", "Ascites", "Spiders",
"Bilirubin_cat", "Cholesterol_cat", "Alk_Phos_cat",
"SGOT_cat", "Platelets_cat", "Prothrombin_cat", "Age_cat")
# Log-log Survival Plots dan Transformasi
for (var in variables) {
df_clean = df[!is.na(df[[var]]), ]
fit = survfit(y ~ df_clean[[var]], data = df_clean)
plot(fit,
fun = minusloglog,
main = paste("Log-Log Survival Plot by", var),
xlab = "Time (Days)",
ylab = "-log-log S",
col = 1:length(unique(df_clean[[var]])),
lty = 1,
conf.int = FALSE)
legend("topright",
legend = levels(df_clean[[var]]),
col = 1:length(unique(df_clean[[var]])),
lty = 1,
title = var)
# Summary
summary_km = summary(fit)
time = summary_km$time
surv = summary_km$surv
loglog_surv = minusloglog(surv)
n_risk = summary_km$n.risk
n_censor = summary_km$n.censor
n_event = summary_km$n.event
# Simpan hasil transformasi ke dalam list
results_list[[var]] = data.frame(
Time = time,
N_at_risk = n_risk,
Censored = n_censor,
Observed = n_event,
Survival = surv,
MinusLogLog_S = loglog_surv
)
}
variables = c("Drug", "Sex", "Ascites", "Spiders",
"Bilirubin_cat", "Cholesterol_cat", "Alk_Phos_cat",
"SGOT_cat", "Platelets_cat", "Prothrombin_cat", "Age_cat")
# Print hasil transformasi untuk setiap variabel
for (var in variables) {
cat("\nHasil transformasi minus log-log untuk variabel:", var, "\n")
print(results_list[[var]])
}##
## Hasil transformasi minus log-log untuk variabel: Drug
## Time N_at_risk Censored Observed Survival MinusLogLog_S
## 1 41 136 0 1 0.9926471 4.90896710
## 2 71 135 0 1 0.9852941 4.21210931
## 3 131 134 0 1 0.9779412 3.80291045
## 4 140 133 0 1 0.9705882 3.51147118
## 5 179 132 0 1 0.9632353 3.28454665
## 6 198 131 0 1 0.9558824 3.09842002
## 7 223 130 0 1 0.9485294 2.94043984
## 8 334 129 0 1 0.9411765 2.80305417
## 9 348 128 0 1 0.9338235 2.68139173
## 10 388 127 0 1 0.9264706 2.57212633
## 11 400 126 0 1 0.9191176 2.47288541
## 12 515 125 0 1 0.9117647 2.38191709
## 13 673 123 1 1 0.9043520 2.29723313
## 14 694 122 0 1 0.8969393 2.21854611
## 15 750 119 2 1 0.8894020 2.14382222
## 16 762 118 0 1 0.8818647 2.07372441
## 17 799 117 0 1 0.8743273 2.00767596
## 18 904 113 3 1 0.8665899 1.94358719
## 19 980 111 1 1 0.8587828 1.88230221
## 20 999 110 0 1 0.8509757 1.82404489
## 21 1012 109 0 1 0.8431686 1.76850191
## 22 1077 108 0 1 0.8353615 1.71540558
## 23 1083 107 0 1 0.8275544 1.66452531
## 24 1152 106 0 1 0.8197472 1.61566108
## 25 1170 104 1 1 0.8118651 1.56819453
## 26 1191 103 0 2 0.7961007 1.47827978
## 27 1235 99 2 1 0.7880593 1.43472026
## 28 1297 96 2 1 0.7798503 1.39169579
## 29 1360 93 2 1 0.7714648 1.34913654
## 30 1434 88 4 1 0.7626982 1.30603175
## 31 1576 82 5 1 0.7533970 1.26173262
## 32 1657 78 3 1 0.7437380 1.21717155
## 33 1682 77 0 1 0.7340791 1.17396596
## 34 1690 76 0 2 0.7147612 1.09121952
## 35 1741 72 2 1 0.7048340 1.05041383
## 36 1827 67 4 1 0.6943141 1.00832148
## 37 1925 63 3 1 0.6832932 0.96539909
## 38 2055 57 5 1 0.6713056 0.91997059
## 39 2105 56 0 1 0.6593180 0.87575054
## 40 2224 54 1 1 0.6471084 0.83185446
## 41 2256 52 1 1 0.6346640 0.78820653
## 42 2288 50 1 1 0.6219708 0.74473064
## 43 2297 49 0 1 0.6092775 0.70222530
## 44 2386 43 5 1 0.5951082 0.65582832
## 45 2400 42 0 1 0.5809390 0.61044427
## 46 2540 37 4 1 0.5652379 0.56122721
## 47 2583 35 1 1 0.5490883 0.51166591
## 48 2598 34 0 1 0.5329386 0.46306932
## 49 2689 32 1 1 0.5162843 0.41385364
## 50 3086 22 9 1 0.4928168 0.34585128
## 51 3282 18 3 1 0.4654381 0.26817213
## 52 3574 15 2 1 0.4344089 0.18179889
## 53 3584 14 0 1 0.4033797 0.09664640
## 54 4079 6 7 1 0.3361498 -0.08635982
## 55 4191 5 0 1 0.2689198 -0.27257510
## 56 51 140 0 1 0.9928571 4.93806032
## 57 77 139 0 1 0.9857143 4.24130950
## 58 110 138 0 1 0.9785714 3.83221894
## 59 186 137 0 1 0.9714286 3.54088930
## 60 191 136 0 1 0.9642857 3.31407580
## 61 216 135 0 1 0.9571429 3.12806159
## 62 264 134 0 1 0.9500000 2.97019525
## 63 304 133 0 1 0.9428571 2.83292489
## 64 321 132 0 1 0.9357143 2.71137925
## 65 326 131 0 1 0.9285714 2.60223217
## 66 460 130 0 1 0.9214286 2.50311113
## 67 549 129 0 1 0.9142857 2.41226427
## 68 552 128 0 1 0.9071429 2.32836110
## 69 597 127 0 1 0.9000000 2.25036733
## 70 611 126 0 1 0.8928571 2.17746296
## 71 733 125 0 1 0.8857143 2.10898688
## 72 769 124 0 1 0.8785714 2.04439826
## 73 786 123 0 1 0.8714286 1.98324900
## 74 790 121 1 1 0.8642267 1.92469551
## 75 797 120 0 1 0.8570248 1.86893150
## 76 850 118 1 1 0.8497619 1.81523823
## 77 853 117 0 1 0.8424989 1.76385526
## 78 859 116 0 1 0.8352360 1.71457108
## 79 890 115 0 1 0.8279731 1.66720153
## 80 930 114 0 1 0.8207102 1.62158521
## 81 943 113 0 1 0.8134473 1.57757989
## 82 974 112 0 1 0.8061843 1.53505959
## 83 1080 108 3 1 0.7987197 1.49278776
## 84 1165 105 2 1 0.7911128 1.45109021
## 85 1212 104 0 1 0.7835060 1.41068293
## 86 1356 94 9 1 0.7751708 1.36777924
## 87 1413 92 1 1 0.7667450 1.32576022
## 88 1427 89 2 1 0.7581299 1.28409696
## 89 1444 86 2 1 0.7493144 1.24272554
## 90 1487 84 1 1 0.7403940 1.20206545
## 91 1536 82 1 1 0.7313648 1.16205430
## 92 1786 71 10 1 0.7210639 1.11771107
## 93 1847 68 2 1 0.7104600 1.07340498
## 94 2090 62 5 1 0.6990010 1.02693438
## 95 2419 50 11 1 0.6850210 0.97205243
## 96 2466 47 2 1 0.6704461 0.91676084
## 97 2769 37 9 1 0.6523259 0.85047730
## 98 2796 35 1 1 0.6336880 0.78482723
## 99 2847 32 2 1 0.6138853 0.71754806
## 100 3090 30 1 1 0.5934224 0.65037746
## 101 3170 27 2 1 0.5714438 0.58055253
## 102 3244 26 0 1 0.5494652 0.51281126
## 103 3358 24 1 1 0.5265708 0.44414967
## 104 3395 22 1 1 0.5026358 0.37412716
## 105 3428 20 1 1 0.4775040 0.30221013
## 106 3445 19 1 1 0.4523722 0.23161691
## 107 3762 13 4 1 0.4175744 0.13548455
## 108 3839 11 1 1 0.3796130 0.03190062
## 109 3853 10 0 1 0.3416517 -0.07135587
##
## Hasil transformasi minus log-log untuk variabel: Sex
## Time N_at_risk Censored Observed Survival MinusLogLog_S
## 1 41 242 0 1 0.9958678 5.486868044
## 2 51 241 0 1 0.9917355 4.791644014
## 3 71 240 0 1 0.9876033 4.384094834
## 4 77 239 0 1 0.9834711 4.094321413
## 5 110 238 0 1 0.9793388 3.869079183
## 6 131 237 0 1 0.9752066 3.684651559
## 7 179 236 0 1 0.9710744 3.528387369
## 8 186 235 0 1 0.9669421 3.392734965
## 9 198 234 0 1 0.9628099 3.272823360
## 10 216 233 0 1 0.9586777 3.165326658
## 11 223 232 0 1 0.9545455 3.067872615
## 12 264 231 0 1 0.9504132 2.978709640
## 13 304 230 0 1 0.9462810 2.896507537
## 14 321 229 0 1 0.9421488 2.820232311
## 15 326 228 0 1 0.9380165 2.749064266
## 16 334 227 0 1 0.9338843 2.682342588
## 17 348 226 0 1 0.9297521 2.619526764
## 18 388 225 0 1 0.9256198 2.560169036
## 19 400 224 0 1 0.9214876 2.503894324
## 20 460 223 0 1 0.9173554 2.450385297
## 21 515 222 0 1 0.9132231 2.399371090
## 22 549 221 0 1 0.9090909 2.350618656
## 23 597 220 0 1 0.9049587 2.303926028
## 24 673 219 0 1 0.9008264 2.259117034
## 25 694 218 0 1 0.8966942 2.216037074
## 26 733 216 1 1 0.8925429 2.174361179
## 27 750 214 1 1 0.8883721 2.133985575
## 28 769 213 0 1 0.8842013 2.094997978
## 29 786 212 0 1 0.8800306 2.057299428
## 30 790 210 1 1 0.8758399 2.020630045
## 31 797 209 0 1 0.8716493 1.985091206
## 32 850 206 2 1 0.8674180 1.950279761
## 33 853 205 0 1 0.8631867 1.916477745
## 34 859 204 0 1 0.8589554 1.883622984
## 35 904 202 1 1 0.8547031 1.851502698
## 36 930 201 0 1 0.8504509 1.820229284
## 37 943 199 1 1 0.8461773 1.789603399
## 38 974 198 0 1 0.8419036 1.759739419
## 39 980 197 0 1 0.8376300 1.730596051
## 40 1080 193 3 1 0.8332900 1.701698016
## 41 1083 192 0 1 0.8289499 1.673467061
## 42 1165 188 3 1 0.8245406 1.645433668
## 43 1170 187 0 1 0.8201313 1.618020534
## 44 1191 186 0 2 0.8113127 1.564934366
## 45 1212 184 0 1 0.8069034 1.539206214
## 46 1235 179 4 1 0.8023955 1.513429807
## 47 1356 169 9 1 0.7976476 1.486829503
## 48 1413 163 5 1 0.7927541 1.459974494
## 49 1427 160 2 1 0.7877994 1.433336446
## 50 1434 158 2 1 0.7828133 1.407064417
## 51 1444 155 1 1 0.7777629 1.380974324
## 52 1487 151 3 1 0.7726122 1.354880596
## 53 1576 144 6 1 0.7672468 1.328226358
## 54 1657 139 4 1 0.7617270 1.301339381
## 55 1690 137 1 2 0.7506069 1.248715109
## 56 1741 132 3 1 0.7449205 1.222551796
## 57 1786 126 5 1 0.7390084 1.195852761
## 58 1827 123 2 1 0.7330002 1.169219618
## 59 1847 120 2 1 0.7268919 1.142634742
## 60 1925 116 3 1 0.7206256 1.115853407
## 61 2055 107 8 1 0.7138908 1.087597297
## 62 2090 106 0 1 0.7071560 1.059860809
## 63 2105 105 0 1 0.7004211 1.032618142
## 64 2224 98 6 1 0.6932740 1.004220744
## 65 2256 95 2 1 0.6859764 0.975743281
## 66 2288 93 1 1 0.6786003 0.947464076
## 67 2297 91 1 1 0.6711431 0.919363322
## 68 2400 82 8 1 0.6629585 0.889057586
## 69 2419 81 0 1 0.6547738 0.859283320
## 70 2466 76 4 1 0.6461583 0.828484341
## 71 2540 71 4 1 0.6370575 0.796520063
## 72 2583 66 4 1 0.6274051 0.763220357
## 73 2598 65 0 1 0.6177527 0.730502310
## 74 2769 58 6 1 0.6071018 0.695031503
## 75 2847 54 3 1 0.5958592 0.658261120
## 76 3086 45 8 1 0.5826179 0.615771939
## 77 3090 44 0 1 0.5693766 0.574096978
## 78 3170 38 5 1 0.5543930 0.527833644
## 79 3244 37 0 1 0.5394094 0.482431813
## 80 3282 35 1 1 0.5239977 0.436541014
## 81 3358 32 2 1 0.5076228 0.388583376
## 82 3428 29 2 1 0.4901185 0.338122385
## 83 3445 28 0 1 0.4726143 0.288381446
## 84 3574 27 0 1 0.4551101 0.239252632
## 85 3584 24 2 1 0.4361471 0.186599981
## 86 3762 20 3 1 0.4143398 0.126619461
## 87 3839 18 1 1 0.3913209 0.063763031
## 88 3853 17 0 1 0.3683020 0.001148734
## 89 140 34 0 1 0.9705882 3.511471176
## 90 191 33 0 1 0.9411765 2.803054168
## 91 552 31 1 1 0.9108159 2.370709292
## 92 611 30 0 1 0.8804554 2.061083123
## 93 762 29 0 1 0.8500949 1.817647802
## 94 799 28 0 1 0.8197343 1.615581947
## 95 890 26 1 1 0.7882061 1.435502784
## 96 999 25 0 1 0.7566779 1.277197231
## 97 1012 24 0 1 0.7251496 1.135139511
## 98 1077 23 0 1 0.6936214 1.005589145
## 99 1152 22 0 1 0.6620931 0.885885054
## 100 1297 21 0 1 0.6305649 0.774055276
## 101 1360 18 2 1 0.5955335 0.657205610
## 102 1536 17 0 1 0.5605021 0.546587076
## 103 1682 15 1 1 0.5231353 0.433995560
## 104 2386 11 3 1 0.4755776 0.296756034
## 105 2689 10 0 1 0.4280198 0.164184048
## 106 2796 9 0 1 0.3804620 0.034209672
## 107 3395 7 1 1 0.3261103 -0.113792455
## 108 4079 4 2 1 0.2445827 -0.342313440
## 109 4191 3 0 1 0.1630552 -0.595350610
##
## Hasil transformasi minus log-log untuk variabel: Ascites
## Time N_at_risk Censored Observed Survival MinusLogLog_S
## 1 71 257 0 1 0.9961089 5.547127398
## 2 131 256 0 1 0.9922179 4.852025178
## 3 140 255 0 1 0.9883268 4.444598632
## 4 186 254 0 1 0.9844358 4.154948679
## 5 198 253 0 1 0.9805447 3.929830757
## 6 304 252 0 1 0.9766537 3.745528295
## 7 321 251 0 1 0.9727626 3.589390127
## 8 326 250 0 1 0.9688716 3.453864616
## 9 460 249 0 1 0.9649805 3.334080785
## 10 515 248 0 1 0.9610895 3.226712747
## 11 552 246 1 1 0.9571826 3.129010586
## 12 597 245 0 1 0.9532758 3.039662032
## 13 611 244 0 1 0.9493689 2.957322642
## 14 673 243 0 1 0.9454620 2.880948254
## 15 694 242 0 1 0.9415552 2.809711721
## 16 733 240 1 1 0.9376320 2.742677017
## 17 750 238 1 1 0.9336924 2.679342421
## 18 762 237 0 1 0.9297527 2.619536830
## 19 769 236 0 1 0.9258131 2.562874051
## 20 786 235 0 1 0.9218735 2.509027876
## 21 790 233 1 1 0.9179169 2.457505175
## 22 797 232 0 1 0.9139604 2.408300966
## 23 799 231 0 1 0.9100039 2.361205957
## 24 850 228 2 1 0.9060126 2.315649699
## 25 853 227 0 1 0.9020214 2.271890482
## 26 859 226 0 1 0.8980301 2.229784065
## 27 890 224 1 1 0.8940211 2.189024897
## 28 904 222 1 1 0.8899940 2.149515461
## 29 930 221 0 1 0.8859668 2.111338750
## 30 943 219 1 1 0.8819213 2.074235638
## 31 974 218 0 1 0.8778758 2.038298493
## 32 980 217 0 1 0.8738303 2.003450676
## 33 999 215 1 1 0.8697660 1.969467742
## 34 1012 214 0 1 0.8657016 1.936450368
## 35 1077 211 2 1 0.8615988 1.904040174
## 36 1080 210 0 1 0.8574959 1.872500001
## 37 1083 209 0 1 0.8533931 1.841779852
## 38 1152 206 2 1 0.8492504 1.811546779
## 39 1165 204 1 1 0.8450874 1.781916804
## 40 1170 203 0 1 0.8409244 1.752999484
## 41 1212 202 0 1 0.8367614 1.724757402
## 42 1235 197 4 1 0.8325139 1.696601932
## 43 1297 192 4 1 0.8281779 1.668512482
## 44 1356 184 7 1 0.8236769 1.640016200
## 45 1360 183 0 1 0.8191760 1.612159786
## 46 1413 177 5 1 0.8145479 1.584149852
## 47 1427 174 2 1 0.8098665 1.556438487
## 48 1444 170 3 1 0.8051026 1.528846756
## 49 1487 166 3 1 0.8002526 1.501355804
## 50 1536 163 2 1 0.7953431 1.474113148
## 51 1576 158 4 1 0.7903093 1.446762510
## 52 1657 152 5 1 0.7851099 1.419100148
## 53 1682 150 1 1 0.7798758 1.391827200
## 54 1690 149 0 2 0.7694077 1.338898201
## 55 1741 144 3 1 0.7640646 1.312661079
## 56 1786 138 5 1 0.7585279 1.285993899
## 57 1827 135 2 1 0.7529091 1.259447685
## 58 1847 132 2 1 0.7472053 1.233005760
## 59 1925 128 3 1 0.7413677 1.206447487
## 60 2055 118 9 1 0.7350850 1.178405119
## 61 2090 117 0 1 0.7288022 1.150896770
## 62 2105 116 0 1 0.7225194 1.123896223
## 63 2224 109 7 1 0.7158908 1.095932889
## 64 2256 105 2 1 0.7090728 1.067703577
## 65 2288 103 1 1 0.7021886 1.039721055
## 66 2297 101 1 1 0.6952362 1.011966021
## 67 2386 92 8 1 0.6876793 0.982343221
## 68 2400 91 0 1 0.6801224 0.953259411
## 69 2419 90 0 1 0.6725655 0.924686306
## 70 2466 85 4 1 0.6646529 0.895287145
## 71 2540 80 4 1 0.6563448 0.864958401
## 72 2583 75 4 1 0.6475935 0.833577543
## 73 2598 74 0 1 0.6388422 0.802743900
## 74 2689 69 4 1 0.6295837 0.770683861
## 75 2769 66 2 1 0.6200445 0.738219838
## 76 2796 64 1 1 0.6103563 0.705802180
## 77 2847 61 2 1 0.6003505 0.672870801
## 78 3086 51 9 1 0.5885789 0.634794694
## 79 3170 45 5 1 0.5754994 0.593270798
## 80 3244 44 0 1 0.5624198 0.552504457
## 81 3282 42 1 1 0.5490289 0.511485468
## 82 3358 39 2 1 0.5349512 0.469076523
## 83 3395 37 1 1 0.5204931 0.426210793
## 84 3428 35 1 1 0.5056218 0.382775151
## 85 3445 34 1 1 0.4907506 0.339931354
## 86 3574 32 0 1 0.4754147 0.296295215
## 87 3584 29 2 1 0.4590211 0.250181809
## 88 3762 25 3 1 0.4406602 0.199083825
## 89 3839 22 2 1 0.4206302 0.143868970
## 90 3853 21 0 1 0.4006002 0.089059243
## 91 4079 14 6 1 0.3719859 0.011162723
## 92 4191 10 3 1 0.3347873 -0.090078212
## 93 41 19 0 1 0.9473684 2.917527168
## 94 51 18 0 1 0.8947368 2.196194392
## 95 77 17 0 1 0.8421053 1.761131781
## 96 110 16 0 1 0.7894737 1.442277465
## 97 179 15 0 1 0.7368421 1.186192975
## 98 191 14 0 1 0.6842105 0.968928030
## 99 216 13 0 1 0.6315789 0.777545982
## 100 223 12 0 1 0.5789474 0.604141000
## 101 264 11 0 1 0.5263158 0.443394593
## 102 334 10 0 1 0.4736842 0.291403118
## 103 348 9 0 1 0.4210526 0.145028734
## 104 388 8 0 1 0.3684211 0.001472253
## 105 400 7 0 1 0.3157895 -0.142089241
## 106 549 6 0 1 0.2631579 -0.288932091
## 107 1191 5 0 2 0.1578947 -0.612927248
## 108 1434 3 0 1 0.1052632 -0.811504184
## 109 3090 1 1 1 0.0000000 -Inf
##
## Hasil transformasi minus log-log untuk variabel: Spiders
## Time N_at_risk Censored Observed Survival MinusLogLog_S
## 1 41 196 0 1 0.99489796 5.275558199
## 2 191 195 0 1 0.98979592 4.579843612
## 3 198 194 0 1 0.98469388 4.171800048
## 4 223 193 0 1 0.97959184 3.881528369
## 5 334 192 0 1 0.97448980 3.655783955
## 6 348 191 0 1 0.96938776 3.470850172
## 7 388 190 0 1 0.96428571 3.314075796
## 8 552 188 1 1 0.95915653 3.177230406
## 9 597 187 0 1 0.95402736 3.056269567
## 10 611 186 0 1 0.94889818 2.947822841
## 11 733 184 1 1 0.94374112 2.848979404
## 12 769 183 0 1 0.93858407 2.758561920
## 13 786 182 0 1 0.93342701 2.675207840
## 14 790 181 0 1 0.92826996 2.597860070
## 15 797 180 0 1 0.92311290 2.525681974
## 16 799 179 0 1 0.91795584 2.458000031
## 17 853 177 1 1 0.91276965 2.393914213
## 18 890 175 1 1 0.90755383 2.333019543
## 19 904 173 1 1 0.90230785 2.274974603
## 20 930 172 0 1 0.89706187 2.219803621
## 21 943 170 1 1 0.89178504 2.166917071
## 22 974 169 0 1 0.88650821 2.116396879
## 23 999 167 1 1 0.88119977 2.067742869
## 24 1012 166 0 1 0.87589134 2.021072747
## 25 1077 163 2 1 0.87051777 1.975679174
## 26 1080 162 0 1 0.86514421 1.931993930
## 27 1152 159 2 1 0.85970305 1.889361886
## 28 1191 157 1 1 0.85422723 1.847961389
## 29 1212 156 0 1 0.84875142 1.807956359
## 30 1297 150 5 1 0.84309308 1.767977023
## 31 1356 144 5 1 0.83723826 1.727959171
## 32 1360 143 0 1 0.83138345 1.689216459
## 33 1427 136 6 1 0.82527034 1.650029379
## 34 1487 129 6 1 0.81887289 1.610306255
## 35 1536 127 1 1 0.81242507 1.571508482
## 36 1682 119 7 1 0.80559797 1.531688039
## 37 1690 118 0 1 0.79877087 1.493073016
## 38 1741 115 2 1 0.79182504 1.454938045
## 39 1786 111 3 1 0.78469148 1.416899310
## 40 2055 97 13 1 0.77660188 1.375048049
## 41 2090 96 0 1 0.76851227 1.334465971
## 42 2256 88 7 1 0.75977918 1.291975789
## 43 2288 86 1 1 0.75094454 1.250283854
## 44 2297 84 1 1 0.74200472 1.209321504
## 45 2386 79 4 1 0.73261226 1.167516487
## 46 2419 78 0 1 0.72321979 1.126881820
## 47 2466 73 4 1 0.71331267 1.085196406
## 48 2583 65 7 1 0.70233863 1.040325608
## 49 2598 64 0 1 0.69136459 0.996720262
## 50 2689 61 2 1 0.68003074 0.952909925
## 51 2769 58 2 1 0.66830607 0.908796348
## 52 2796 56 1 1 0.65637204 0.865057079
## 53 2847 53 2 1 0.64398766 0.820808487
## 54 3086 44 8 1 0.62935158 0.769887342
## 55 3090 43 0 1 0.61471549 0.720321622
## 56 3170 37 5 1 0.59810156 0.665542315
## 57 3282 35 1 1 0.58101294 0.610678663
## 58 3358 33 1 1 0.56340649 0.555554719
## 59 3395 31 1 1 0.54523209 0.499978462
## 60 3574 30 0 1 0.52705769 0.445591602
## 61 3584 27 2 1 0.50753703 0.388334278
## 62 3839 21 5 1 0.48336860 0.318862135
## 63 3853 20 0 1 0.45920017 0.250682977
## 64 4079 13 6 1 0.42387708 0.152787878
## 65 4191 9 3 1 0.37677963 0.024195561
## 66 51 80 0 1 0.98750000 4.375743836
## 67 71 79 0 1 0.97500000 3.676247258
## 68 77 78 0 1 0.96250000 3.264364608
## 69 110 77 0 1 0.95000000 2.970195249
## 70 131 76 0 1 0.93750000 2.740493007
## 71 140 75 0 1 0.92500000 2.551539632
## 72 179 74 0 1 0.91250000 2.390682221
## 73 186 73 0 1 0.90000000 2.250367327
## 74 216 72 0 1 0.88750000 2.125722093
## 75 264 71 0 1 0.87500000 2.013418678
## 76 304 70 0 1 0.86250000 1.911082813
## 77 321 69 0 1 0.85000000 1.816960795
## 78 326 68 0 1 0.83750000 1.729720233
## 79 400 67 0 1 0.82500000 1.648324840
## 80 460 66 0 1 0.81250000 1.571952527
## 81 515 65 0 1 0.80000000 1.499939987
## 82 549 64 0 1 0.78750000 1.431744096
## 83 673 63 0 1 0.77500000 1.366914374
## 84 694 62 0 1 0.76250000 1.305072888
## 85 750 60 1 1 0.74979167 1.244934083
## 86 762 59 0 1 0.73708333 1.187265413
## 87 850 56 2 1 0.72392113 1.129877508
## 88 859 55 0 1 0.71075893 1.074636129
## 89 980 54 0 1 0.69759673 1.021334355
## 90 1083 53 0 1 0.68443452 0.969790948
## 91 1165 52 0 1 0.67127232 0.919846094
## 92 1170 51 0 1 0.65811012 0.871357980
## 93 1191 50 0 1 0.64494792 0.824200014
## 94 1235 47 2 1 0.63122562 0.776328983
## 95 1413 42 4 1 0.61619644 0.725278979
## 96 1434 41 0 1 0.60116726 0.675538951
## 97 1444 40 0 1 0.58613808 0.626985180
## 98 1576 35 4 1 0.56939127 0.574142795
## 99 1657 34 0 1 0.55264447 0.522492767
## 100 1690 33 0 1 0.53589767 0.471906157
## 101 1827 28 4 1 0.51675847 0.415243216
## 102 1847 27 0 1 0.49761926 0.359650763
## 103 1925 25 1 1 0.47771449 0.302806516
## 104 2105 22 2 1 0.45600020 0.241737817
## 105 2224 19 2 1 0.43200019 0.175152213
## 106 2400 14 4 1 0.40114303 0.090540623
## 107 2540 13 0 1 0.37028588 0.006541414
## 108 3244 8 4 1 0.32400014 -0.119569285
## 109 3428 5 2 1 0.25920011 -0.300219310
## 110 3445 4 1 1 0.19440008 -0.493376439
## 111 3762 2 0 1 0.09720004 -0.846290552
##
## Hasil transformasi minus log-log untuk variabel: Bilirubin_cat
## Time N_at_risk Censored Observed Survival MinusLogLog_S
## 1 198 115 0 1 0.9913043 4.74056847
## 2 515 114 0 1 0.9826087 4.04302562
## 3 694 113 0 1 0.9739130 3.63313232
## 4 1682 90 22 1 0.9630918 3.28057684
## 5 1786 82 7 1 0.9513468 2.99820232
## 6 1847 78 3 1 0.9391500 2.76811785
## 7 2055 70 7 1 0.9257336 2.56176015
## 8 2090 69 0 1 0.9123172 2.38849607
## 9 2224 64 5 1 0.8980622 2.23011608
## 10 2297 58 4 1 0.8825784 2.08018028
## 11 2419 54 3 1 0.8662343 1.94072484
## 12 2466 51 2 1 0.8492493 1.81153909
## 13 2583 47 3 1 0.8311802 1.68789332
## 14 2598 46 0 1 0.8131111 1.57557980
## 15 2769 42 3 1 0.7937513 1.46540201
## 16 3086 32 9 1 0.7689466 1.33661387
## 17 3170 26 5 1 0.7393717 1.19747885
## 18 3584 18 7 1 0.6982955 1.02411834
## 19 3853 13 4 1 0.6445804 0.82290138
## 20 41 161 0 1 0.9937888 5.07829071
## 21 51 160 0 1 0.9875776 4.38201361
## 22 71 159 0 1 0.9813665 3.97340215
## 23 77 158 0 1 0.9751553 3.68255710
## 24 110 157 0 1 0.9689441 3.45623374
## 25 131 156 0 1 0.9627329 3.27071537
## 26 140 155 0 1 0.9565217 3.11335066
## 27 179 154 0 1 0.9503106 2.97658783
## 28 186 153 0 1 0.9440994 2.85555574
## 29 191 152 0 1 0.9378882 2.74692833
## 30 216 151 0 1 0.9316770 2.64833323
## 31 223 150 0 1 0.9254658 2.55801866
## 32 264 149 0 1 0.9192547 2.47465429
## 33 304 148 0 1 0.9130435 2.39720595
## 34 321 147 0 1 0.9068323 2.32485378
## 35 326 146 0 1 0.9006211 2.25693679
## 36 334 145 0 1 0.8944099 2.19291429
## 37 348 144 0 1 0.8881988 2.13233835
## 38 388 143 0 1 0.8819876 2.07483371
## 39 400 142 0 1 0.8757764 2.02008285
## 40 460 141 0 1 0.8695652 1.96781472
## 41 549 139 1 1 0.8633094 1.91744385
## 42 552 138 0 1 0.8570535 1.86914853
## 43 597 137 0 1 0.8507976 1.82274878
## 44 611 136 0 1 0.8445418 1.77808679
## 45 673 135 0 1 0.8382859 1.73502338
## 46 733 133 1 1 0.8319830 1.69312782
## 47 750 131 1 1 0.8256320 1.65231330
## 48 762 130 0 1 0.8192810 1.61280257
## 49 769 129 0 1 0.8129299 1.57450358
## 50 786 128 0 1 0.8065789 1.53733347
## 51 790 126 1 1 0.8001775 1.50093475
## 52 797 125 0 1 0.7937761 1.46553733
## 53 799 124 0 1 0.7873747 1.43107807
## 54 850 121 2 1 0.7808674 1.39695146
## 55 853 120 0 1 0.7743602 1.36367953
## 56 859 119 0 1 0.7678530 1.33121169
## 57 890 117 1 1 0.7612901 1.29923363
## 58 904 115 1 1 0.7546702 1.26771373
## 59 930 114 0 1 0.7480503 1.23689193
## 60 943 112 1 1 0.7413713 1.20646352
## 61 974 111 0 1 0.7346923 1.17667043
## 62 980 110 0 1 0.7280132 1.14747895
## 63 999 109 0 1 0.7213342 1.11885779
## 64 1012 108 0 1 0.7146552 1.09077782
## 65 1077 106 1 1 0.7079132 1.06295419
## 66 1080 105 0 1 0.7011711 1.03562826
## 67 1083 104 0 1 0.6944291 1.00877564
## 68 1152 102 1 1 0.6876210 0.98211679
## 69 1165 101 0 1 0.6808129 0.95589520
## 70 1170 100 0 1 0.6740047 0.93009013
## 71 1191 99 0 2 0.6603885 0.87965263
## 72 1212 97 0 1 0.6535803 0.85498444
## 73 1235 94 2 1 0.6466274 0.83014722
## 74 1297 92 1 1 0.6395988 0.80538872
## 75 1356 87 4 1 0.6322471 0.77984953
## 76 1360 86 0 1 0.6248954 0.75465876
## 77 1413 81 4 1 0.6171806 0.72858049
## 78 1427 78 2 1 0.6092681 0.70219407
## 79 1434 77 0 1 0.6013555 0.67615431
## 80 1444 75 1 1 0.5933374 0.65010291
## 81 1487 73 1 1 0.5852095 0.62402161
## 82 1536 71 1 1 0.5769671 0.59789152
## 83 1576 68 2 1 0.5684823 0.57130997
## 84 1657 64 3 1 0.5595998 0.54380784
## 85 1690 62 1 2 0.5415482 0.48886311
## 86 1741 60 0 1 0.5325224 0.46182850
## 87 1827 56 3 1 0.5230130 0.43363482
## 88 1925 52 3 1 0.5129551 0.40411554
## 89 2105 49 2 1 0.5024866 0.37369573
## 90 2256 46 2 1 0.4915630 0.34225768
## 91 2288 45 0 1 0.4806394 0.31110351
## 92 2386 39 5 1 0.4683153 0.27626282
## 93 2400 38 0 1 0.4559912 0.24171268
## 94 2540 33 4 1 0.4421733 0.20327545
## 95 2689 28 4 1 0.4263814 0.15967465
## 96 2796 25 2 1 0.4093261 0.11289654
## 97 2847 23 1 1 0.3915293 0.06433072
## 98 3090 20 2 1 0.3719529 0.01107295
## 99 3244 19 0 1 0.3523764 -0.04215424
## 100 3282 18 0 1 0.3327999 -0.09550449
## 101 3358 16 1 1 0.3119999 -0.15250843
## 102 3395 15 0 1 0.2911999 -0.21005438
## 103 3428 14 0 1 0.2703999 -0.26838696
## 104 3445 13 0 1 0.2496000 -0.32778881
## 105 3574 12 0 1 0.2288000 -0.38859508
## 106 3762 11 0 1 0.2080000 -0.45121408
## 107 3839 9 1 1 0.1848889 -0.52354465
## 108 4079 5 3 1 0.1479111 -0.64770200
## 109 4191 4 0 1 0.1109333 -0.78792360
##
## Hasil transformasi minus log-log untuk variabel: Cholesterol_cat
## Time N_at_risk Censored Observed Survival MinusLogLog_S
## 1 41 20 0 1 0.9500000 2.97019525
## 2 77 19 0 1 0.9000000 2.25036733
## 3 110 18 0 1 0.8500000 1.81696079
## 4 131 17 0 1 0.8000000 1.49993999
## 5 140 16 0 1 0.7500000 1.24589932
## 6 191 15 0 1 0.7000000 1.03093043
## 7 348 14 0 1 0.6500000 0.84215099
## 8 552 13 0 1 0.6000000 0.67172699
## 9 1012 12 0 1 0.5500000 0.51443714
## 10 1077 11 0 1 0.5000000 0.36651292
## 11 2583 4 6 1 0.3750000 0.01935689
## 12 51 36 0 1 0.9722222 3.56946657
## 13 179 35 0 1 0.9444444 2.86192868
## 14 304 34 0 1 0.9166667 2.44171640
## 15 388 33 0 1 0.8888889 2.13891103
## 16 549 32 0 1 0.8611111 1.90024664
## 17 750 30 1 1 0.8324074 1.69590424
## 18 1576 25 4 1 0.7991111 1.49497023
## 19 2055 20 4 1 0.7591556 1.28899135
## 20 3170 6 13 1 0.6326296 0.78116969
## 21 3584 4 1 1 0.4744722 0.29363012
## 22 71 220 0 1 0.9954545 5.39135050
## 23 186 219 0 1 0.9909091 4.69591760
## 24 198 218 0 1 0.9863636 4.28815802
## 25 216 217 0 1 0.9818182 3.99817264
## 26 223 216 0 1 0.9772727 3.77271690
## 27 264 215 0 1 0.9727273 3.58807417
## 28 321 214 0 1 0.9681818 3.43159327
## 29 326 213 0 1 0.9636364 3.29572254
## 30 334 212 0 1 0.9590909 3.17559096
## 31 400 211 0 1 0.9545455 3.06787262
## 32 460 210 0 1 0.9500000 2.97019525
## 33 515 209 0 1 0.9454545 2.88080724
## 34 597 207 1 1 0.9408871 2.79799530
## 35 611 206 0 1 0.9363197 2.72116157
## 36 673 205 0 1 0.9317523 2.64947569
## 37 694 204 0 1 0.9271849 2.58226883
## 38 733 202 1 1 0.9225949 2.51869010
## 39 762 201 0 1 0.9180048 2.45862375
## 40 769 200 0 1 0.9134148 2.40168576
## 41 786 199 0 1 0.9088248 2.34755163
## 42 790 197 1 1 0.9042115 2.29568876
## 43 797 196 0 1 0.8995981 2.24613749
## 44 799 195 0 1 0.8949848 2.19868899
## 45 850 192 2 1 0.8903234 2.15269667
## 46 853 191 0 1 0.8856621 2.10850118
## 47 859 190 0 1 0.8810007 2.06595786
## 48 890 188 1 1 0.8763145 2.02472444
## 49 904 186 1 1 0.8716031 1.98470557
## 50 930 185 0 1 0.8668918 1.94602220
## 51 943 183 1 1 0.8621547 1.90837912
## 52 974 182 0 1 0.8574175 1.87190551
## 53 980 181 0 1 0.8526804 1.83652399
## 54 999 179 1 1 0.8479169 1.80197531
## 55 1080 176 2 1 0.8430991 1.76801922
## 56 1083 175 0 1 0.8382814 1.73499326
## 57 1152 172 2 1 0.8334077 1.70247298
## 58 1165 170 1 1 0.8285053 1.67061126
## 59 1170 169 0 1 0.8236029 1.63955302
## 60 1191 168 0 2 0.8137981 1.57967059
## 61 1212 166 0 1 0.8088957 1.55076685
## 62 1235 162 3 1 0.8039025 1.52198927
## 63 1297 157 4 1 0.7987821 1.49313579
## 64 1356 150 6 1 0.7934569 1.46379750
## 65 1360 149 0 1 0.7881317 1.43510627
## 66 1413 143 5 1 0.7826203 1.40605784
## 67 1427 140 2 1 0.7770302 1.37723106
## 68 1434 139 1 1 0.7714400 1.34901256
## 69 1444 136 1 1 0.7657677 1.32096935
## 70 1487 134 1 1 0.7600530 1.29328810
## 71 1536 132 1 1 0.7542950 1.26594839
## 72 1657 123 8 1 0.7481625 1.23740874
## 73 1682 121 1 1 0.7419794 1.20920693
## 74 1690 120 0 2 0.7296130 1.15441791
## 75 1741 116 2 1 0.7233233 1.12732340
## 76 1786 109 6 1 0.7166873 1.09926540
## 77 1827 106 2 1 0.7099261 1.07120791
## 78 1847 104 1 1 0.7030999 1.04339609
## 79 1925 100 3 1 0.6960689 1.01526421
## 80 2090 92 7 1 0.6885029 0.98554499
## 81 2105 91 0 1 0.6809369 0.95636926
## 82 2224 87 4 1 0.6731101 0.92672897
## 83 2256 85 0 1 0.6651911 0.89727057
## 84 2288 84 0 1 0.6572722 0.86831740
## 85 2297 83 0 1 0.6493532 0.83984272
## 86 2386 78 4 1 0.6410282 0.81039623
## 87 2400 77 0 1 0.6327032 0.78142354
## 88 2419 76 0 1 0.6243781 0.75289901
## 89 2466 71 4 1 0.6155841 0.72322754
## 90 2540 67 3 1 0.6063962 0.69270397
## 91 2598 62 4 1 0.5966156 0.66071450
## 92 2689 59 2 1 0.5865035 0.62815262
## 93 2769 56 2 1 0.5760302 0.59494099
## 94 2796 55 0 1 0.5655570 0.56221672
## 95 2847 52 2 1 0.5546809 0.52871411
## 96 3086 43 8 1 0.5417813 0.48956518
## 97 3090 42 0 1 0.5288818 0.45100071
## 98 3244 36 5 1 0.5141906 0.40772576
## 99 3282 34 1 1 0.4990673 0.36382296
## 100 3358 31 2 1 0.4829684 0.31772343
## 101 3395 30 0 1 0.4668695 0.27219515
## 102 3428 28 1 1 0.4501955 0.22555490
## 103 3445 27 1 1 0.4335216 0.17934969
## 104 3574 25 0 1 0.4161808 0.13166392
## 105 3762 21 3 1 0.3963626 0.07750140
## 106 3839 18 2 1 0.3743425 0.01756929
## 107 3853 17 0 1 0.3523223 -0.04230132
## 108 4079 11 5 1 0.3202930 -0.12972825
## 109 4191 8 2 1 0.2802564 -0.24063004
##
## Hasil transformasi minus log-log untuk variabel: Alk_Phos_cat
## Time N_at_risk Censored Observed Survival MinusLogLog_S
## 1 41 140 0 1 0.9928571 4.93806032
## 2 51 139 0 1 0.9857143 4.24130950
## 3 110 138 0 1 0.9785714 3.83221894
## 4 131 137 0 1 0.9714286 3.54088930
## 5 140 136 0 1 0.9642857 3.31407580
## 6 179 135 0 1 0.9571429 3.12806159
## 7 186 134 0 1 0.9500000 2.97019525
## 8 191 133 0 1 0.9428571 2.83292489
## 9 304 132 0 1 0.9357143 2.71137925
## 10 348 131 0 1 0.9285714 2.60223217
## 11 515 130 0 1 0.9214286 2.50311113
## 12 552 128 1 1 0.9142299 2.41158338
## 13 750 126 1 1 0.9069741 2.32645405
## 14 799 125 0 1 0.8997183 2.24740078
## 15 850 122 2 1 0.8923436 2.17239897
## 16 980 120 1 1 0.8849074 2.10150487
## 17 1012 118 1 1 0.8774082 2.03421600
## 18 1080 115 2 1 0.8697785 1.96957129
## 19 1191 111 3 1 0.8619427 1.90672267
## 20 1487 95 15 1 0.8528696 1.83791692
## 21 1536 92 2 1 0.8435993 1.77150006
## 22 1576 89 2 1 0.8341206 1.70717629
## 23 1682 84 4 1 0.8241906 1.64323559
## 24 1690 83 0 1 0.8142606 1.58243199
## 25 1741 79 3 1 0.8039535 1.52227995
## 26 1827 73 5 1 0.7929405 1.46098719
## 27 2055 61 11 1 0.7799414 1.39216585
## 28 2090 60 0 1 0.7669424 1.32672989
## 29 2105 59 0 1 0.7539434 1.26429629
## 30 2288 50 8 1 0.7388645 1.19520903
## 31 2419 45 4 1 0.7224453 1.12358080
## 32 2540 38 6 1 0.7034336 1.04474421
## 33 2583 36 1 1 0.6838938 0.96770860
## 34 2598 35 0 1 0.6643540 0.89418634
## 35 2796 31 3 1 0.6429232 0.81705638
## 36 3244 20 10 1 0.6107770 0.70719880
## 37 3395 16 3 1 0.5726035 0.58418185
## 38 3428 15 0 1 0.5344299 0.46751918
## 39 3445 14 1 1 0.4962563 0.35572870
## 40 3762 9 3 1 0.4411167 0.20034820
## 41 71 136 0 1 0.9926471 4.90896710
## 42 77 135 0 1 0.9852941 4.21210931
## 43 198 134 0 1 0.9779412 3.80291045
## 44 216 133 0 1 0.9705882 3.51147118
## 45 223 132 0 1 0.9632353 3.28454665
## 46 264 131 0 1 0.9558824 3.09842002
## 47 321 130 0 1 0.9485294 2.94043984
## 48 326 129 0 1 0.9411765 2.80305417
## 49 334 128 0 1 0.9338235 2.68139173
## 50 388 127 0 1 0.9264706 2.57212633
## 51 400 126 0 1 0.9191176 2.47288541
## 52 460 125 0 1 0.9117647 2.38191709
## 53 549 124 0 1 0.9044118 2.29789082
## 54 597 123 0 1 0.8970588 2.21977231
## 55 611 122 0 1 0.8897059 2.14674150
## 56 673 121 0 1 0.8823529 2.07813725
## 57 694 120 0 1 0.8750000 2.01341868
## 58 733 118 1 1 0.8675847 1.95163191
## 59 762 117 0 1 0.8601695 1.89295649
## 60 769 116 0 1 0.8527542 1.83706721
## 61 786 115 0 1 0.8453390 1.78368673
## 62 790 113 1 1 0.8378581 1.73213384
## 63 797 112 0 1 0.8303772 1.68267987
## 64 853 111 0 1 0.8228964 1.63514030
## 65 859 110 0 1 0.8154155 1.58935344
## 66 890 109 0 1 0.8079346 1.54517680
## 67 904 107 1 1 0.8003838 1.50209182
## 68 930 106 0 1 0.7928330 1.46040325
## 69 943 104 1 1 0.7852096 1.41962550
## 70 974 103 0 1 0.7775862 1.38007085
## 71 999 102 0 1 0.7699628 1.34165364
## 72 1077 101 0 1 0.7623394 1.30429655
## 73 1083 100 0 1 0.7547160 1.26792947
## 74 1152 99 0 1 0.7470927 1.23248866
## 75 1165 98 0 1 0.7394693 1.19791597
## 76 1170 97 0 1 0.7318459 1.16415819
## 77 1191 96 0 1 0.7242225 1.13116652
## 78 1212 95 0 1 0.7165991 1.09889604
## 79 1235 92 2 1 0.7088100 1.06662586
## 80 1297 89 2 1 0.7008458 1.03432177
## 81 1356 85 3 1 0.6926006 1.00157130
## 82 1360 84 0 1 0.6843553 0.96948574
## 83 1413 81 2 1 0.6759065 0.93725762
## 84 1427 79 1 1 0.6673507 0.90525292
## 85 1434 78 1 1 0.6587949 0.87384689
## 86 1444 75 1 1 0.6500110 0.84219024
## 87 1657 70 4 1 0.6407251 0.80933332
## 88 1690 68 1 1 0.6313027 0.77659438
## 89 1786 65 2 1 0.6215903 0.74344304
## 90 1847 63 1 1 0.6117238 0.71034551
## 91 1925 61 1 1 0.6016956 0.67726663
## 92 2224 58 2 1 0.5913215 0.64360415
## 93 2256 55 2 1 0.5805702 0.60927574
## 94 2297 53 1 1 0.5696161 0.57484386
## 95 2386 48 4 1 0.5577491 0.53811778
## 96 2400 47 0 1 0.5458821 0.50194459
## 97 2466 45 1 1 0.5337513 0.46549348
## 98 2689 38 6 1 0.5197053 0.42389370
## 99 2769 35 2 1 0.5048565 0.38055644
## 100 2847 33 1 1 0.4895578 0.33651853
## 101 3086 30 2 1 0.4732393 0.29014617
## 102 3090 29 0 1 0.4569207 0.24430907
## 103 3170 25 3 1 0.4386438 0.19350282
## 104 3282 24 0 1 0.4203670 0.14314646
## 105 3358 22 1 1 0.4012594 0.09085824
## 106 3574 20 1 1 0.3811964 0.03620720
## 107 3584 18 1 1 0.3600189 -0.02136890
## 108 3839 14 3 1 0.3343032 -0.09139966
## 109 3853 13 0 1 0.3085876 -0.16190584
## 110 4079 9 3 1 0.2743001 -0.25737690
## 111 4191 8 0 1 0.2400126 -0.35561916
##
## Hasil transformasi minus log-log untuk variabel: SGOT_cat
## Time N_at_risk Censored Observed Survival MinusLogLog_S
## 1 191 6 0 1 0.8333333 1.70198336
## 2 2466 4 1 1 0.6250000 0.75501486
## 3 3090 1 2 1 0.0000000 -Inf
## 4 41 270 0 1 0.9962963 5.59656724
## 5 51 269 0 1 0.9925926 4.90155959
## 6 71 268 0 1 0.9888889 4.49422822
## 7 77 267 0 1 0.9851852 4.20467406
## 8 110 266 0 1 0.9814815 3.97965254
## 9 131 265 0 1 0.9777778 3.79544710
## 10 140 264 0 1 0.9740741 3.63940660
## 11 179 263 0 1 0.9703704 3.50397938
## 12 186 262 0 1 0.9666667 3.38429449
## 13 198 261 0 1 0.9629630 3.27702605
## 14 216 260 0 1 0.9592593 3.17980182
## 15 223 259 0 1 0.9555556 3.09087024
## 16 264 258 0 1 0.9518519 3.00890112
## 17 304 257 0 1 0.9481481 2.93286049
## 18 321 256 0 1 0.9444444 2.86192868
## 19 326 255 0 1 0.9407407 2.79544487
## 20 334 254 0 1 0.9370370 2.73286859
## 21 348 253 0 1 0.9333333 2.67375209
## 22 388 252 0 1 0.9296296 2.61772031
## 23 400 251 0 1 0.9259259 2.56445594
## 24 460 250 0 1 0.9222222 2.51368814
## 25 515 249 0 1 0.9185185 2.46518387
## 26 549 247 1 1 0.9147998 2.41855712
## 27 552 246 0 1 0.9110811 2.37383044
## 28 597 245 0 1 0.9073624 2.33084750
## 29 611 244 0 1 0.9036437 2.28947041
## 30 673 243 0 1 0.8999250 2.24957695
## 31 694 242 0 1 0.8962063 2.21105828
## 32 733 240 1 1 0.8924721 2.17366442
## 33 750 238 1 1 0.8887223 2.13732052
## 34 762 237 0 1 0.8849724 2.10210559
## 35 769 236 0 1 0.8812225 2.06794669
## 36 786 235 0 1 0.8774726 2.03477763
## 37 790 233 1 1 0.8737066 2.00240178
## 38 797 232 0 1 0.8699406 1.97090799
## 39 799 231 0 1 0.8661747 1.94024539
## 40 850 228 2 1 0.8623757 1.91010863
## 41 853 227 0 1 0.8585766 1.88072630
## 42 859 226 0 1 0.8547776 1.85205792
## 43 890 224 1 1 0.8509617 1.82394265
## 44 904 222 1 1 0.8471285 1.79635274
## 45 930 221 0 1 0.8432953 1.76938349
## 46 943 219 1 1 0.8394447 1.74288531
## 47 974 218 0 1 0.8355940 1.71695403
## 48 980 217 0 1 0.8317434 1.69156295
## 49 999 215 1 1 0.8278748 1.66657261
## 50 1012 214 0 1 0.8240062 1.64207879
## 51 1077 211 2 1 0.8201010 1.61783396
## 52 1080 210 0 1 0.8161957 1.59405140
## 53 1083 209 0 1 0.8122905 1.57071121
## 54 1152 206 2 1 0.8083473 1.54757424
## 55 1165 204 1 1 0.8043848 1.52474078
## 56 1170 203 0 1 0.8004223 1.50230805
## 57 1191 202 0 2 0.7924974 1.45858084
## 58 1212 200 0 1 0.7885349 1.43725664
## 59 1235 196 3 1 0.7845117 1.41595500
## 60 1297 191 4 1 0.7804044 1.39455605
## 61 1356 183 7 1 0.7761399 1.37269702
## 62 1360 182 0 1 0.7718753 1.35118900
## 63 1413 176 5 1 0.7674897 1.32942179
## 64 1427 173 2 1 0.7630533 1.30775180
## 65 1434 171 2 1 0.7585910 1.28629531
## 66 1444 168 1 1 0.7540756 1.26491732
## 67 1487 164 3 1 0.7494776 1.24348018
## 68 1536 161 2 1 0.7448225 1.22210483
## 69 1576 156 4 1 0.7400479 1.20051113
## 70 1657 150 5 1 0.7351143 1.17853482
## 71 1682 148 1 1 0.7301473 1.15674266
## 72 1690 147 0 2 0.7202133 1.11410827
## 73 1741 142 3 1 0.7151414 1.09280423
## 74 1786 135 6 1 0.7098441 1.07087077
## 75 1827 132 2 1 0.7044665 1.04892377
## 76 1847 129 2 1 0.6990055 1.02695220
## 77 1925 125 3 1 0.6934134 1.00476988
## 78 2055 115 9 1 0.6873837 0.98119583
## 79 2090 114 0 1 0.6813541 0.95796416
## 80 2105 113 0 1 0.6753244 0.93506045
## 81 2224 106 7 1 0.6689534 0.91120151
## 82 2256 102 2 1 0.6623950 0.88699125
## 83 2288 100 1 1 0.6557711 0.86288384
## 84 2297 98 1 1 0.6490795 0.83886681
## 85 2386 89 8 1 0.6417865 0.81305846
## 86 2400 88 0 1 0.6344935 0.78761554
## 87 2419 87 0 1 0.6272005 0.76252066
## 88 2540 78 8 1 0.6191594 0.73523562
## 89 2583 73 4 1 0.6106778 0.70686928
## 90 2598 72 0 1 0.6021962 0.67890498
## 91 2689 68 3 1 0.5933403 0.65011233
## 92 2769 65 2 1 0.5842120 0.62084266
## 93 2796 63 1 1 0.5749388 0.59150858
## 94 2847 60 2 1 0.5653565 0.56159487
## 95 3086 51 8 1 0.5542711 0.52746087
## 96 3170 45 5 1 0.5419539 0.49008509
## 97 3244 44 0 1 0.5296368 0.45324282
## 98 3282 42 1 1 0.5170264 0.41602870
## 99 3358 39 2 1 0.5037693 0.37740720
## 100 3395 37 1 1 0.4901539 0.33822365
## 101 3428 35 1 1 0.4761495 0.29837459
## 102 3445 34 1 1 0.4621451 0.25893098
## 103 3574 32 0 1 0.4477031 0.21862254
## 104 3584 29 2 1 0.4322651 0.17588275
## 105 3762 25 3 1 0.4149745 0.12835816
## 106 3839 22 2 1 0.3961120 0.07681806
## 107 3853 21 0 1 0.3772495 0.02547321
## 108 4079 14 6 1 0.3503031 -0.04779583
## 109 4191 10 3 1 0.3152728 -0.14350881
##
## Hasil transformasi minus log-log untuk variabel: Platelets_cat
## Time N_at_risk Censored Observed Survival MinusLogLog_S
## 1 41 32 0 1 0.9687500 3.44990355
## 2 77 31 0 1 0.9375000 2.74049301
## 3 110 30 0 1 0.9062500 2.31830731
## 4 140 29 0 1 0.8750000 2.01341868
## 5 223 28 0 1 0.8437500 1.77255092
## 6 304 27 0 1 0.8125000 1.57195253
## 7 321 26 0 1 0.7812500 1.39893359
## 8 326 25 0 1 0.7500000 1.24589932
## 9 348 24 0 1 0.7187500 1.10793051
## 10 388 23 0 1 0.6875000 0.98164706
## 11 549 22 0 1 0.6562500 0.86461553
## 12 552 21 0 1 0.6250000 0.75501486
## 13 762 20 0 1 0.5937500 0.65143549
## 14 850 19 0 1 0.5625000 0.55275214
## 15 1191 18 0 1 0.5312500 0.45803939
## 16 2105 11 6 1 0.4829545 0.31768401
## 17 2598 6 4 1 0.4024621 0.09414114
## 18 3445 4 1 1 0.3018466 -0.18051690
## 19 4079 2 1 1 0.1509233 -0.63709709
## 20 4191 1 0 1 0.0000000 -Inf
## 21 51 236 0 1 0.9957627 5.46170941
## 22 71 235 0 1 0.9915254 4.76643230
## 23 131 234 0 1 0.9872881 4.35882966
## 24 179 233 0 1 0.9830508 4.06900240
## 25 186 232 0 1 0.9788136 3.84370595
## 26 191 231 0 1 0.9745763 3.65922372
## 27 198 230 0 1 0.9703390 3.50290453
## 28 216 229 0 1 0.9661017 3.36719673
## 29 264 228 0 1 0.9618644 3.24722932
## 30 334 227 0 1 0.9576271 3.13967642
## 31 400 226 0 1 0.9533898 3.04216576
## 32 460 225 0 1 0.9491525 2.95294575
## 33 515 224 0 1 0.9449153 2.87068619
## 34 597 222 1 1 0.9406589 2.79402135
## 35 611 221 0 1 0.9364025 2.72250615
## 36 673 220 0 1 0.9321461 2.65547171
## 37 694 219 0 1 0.9278898 2.59237134
## 38 733 217 1 1 0.9236138 2.53248519
## 39 750 215 1 1 0.9193179 2.47547156
## 40 769 214 0 1 0.9150220 2.42128799
## 41 786 213 0 1 0.9107261 2.36965426
## 42 790 211 1 1 0.9064099 2.32010106
## 43 797 210 0 1 0.9020937 2.27266778
## 44 799 209 0 1 0.8977774 2.22717056
## 45 853 206 2 1 0.8934193 2.18303211
## 46 859 205 0 1 0.8890611 2.14055740
## 47 890 203 1 1 0.8846815 2.09941928
## 48 904 201 1 1 0.8802801 2.05952046
## 49 930 200 0 1 0.8758787 2.02096401
## 50 943 198 1 1 0.8714551 1.98347014
## 51 980 197 0 1 0.8670315 1.94715080
## 52 999 195 1 1 0.8625851 1.91175038
## 53 1012 194 0 1 0.8581388 1.87738673
## 54 1077 191 2 1 0.8536460 1.84365036
## 55 1080 190 0 1 0.8491531 1.81084566
## 56 1083 189 0 1 0.8446602 1.77891717
## 57 1152 186 2 1 0.8401190 1.74748405
## 58 1165 184 1 1 0.8355532 1.71668184
## 59 1170 183 0 1 0.8309873 1.68663882
## 60 1191 182 0 1 0.8264214 1.65731375
## 61 1212 181 0 1 0.8218556 1.62866861
## 62 1235 176 4 1 0.8171859 1.60003908
## 63 1297 171 4 1 0.8124071 1.57140180
## 64 1356 164 6 1 0.8074534 1.54238704
## 65 1360 163 0 1 0.8024997 1.51401937
## 66 1413 158 4 1 0.7974206 1.48557090
## 67 1427 155 2 1 0.7922759 1.45737978
## 68 1434 153 2 1 0.7870976 1.42960702
## 69 1444 150 1 1 0.7818503 1.40204995
## 70 1487 147 2 1 0.7765316 1.37469020
## 71 1536 145 1 1 0.7711762 1.34769546
## 72 1576 140 4 1 0.7656678 1.32048090
## 73 1657 134 5 1 0.7599539 1.29281298
## 74 1682 132 1 1 0.7541966 1.26548608
## 75 1690 131 0 2 0.7426822 1.21238455
## 76 1741 126 3 1 0.7367879 1.18595206
## 77 1786 120 5 1 0.7306480 1.15892462
## 78 1827 117 2 1 0.7244031 1.13193987
## 79 1847 114 2 1 0.7180487 1.10497896
## 80 1925 110 3 1 0.7115210 1.07777979
## 81 2055 101 8 1 0.7044762 1.04896345
## 82 2090 100 0 1 0.6974315 1.02067672
## 83 2224 94 6 1 0.6900120 0.99142845
## 84 2288 90 2 1 0.6823452 0.96175999
## 85 2386 83 6 1 0.6741242 0.93053938
## 86 2400 82 0 1 0.6659031 0.89989822
## 87 2419 81 0 1 0.6576821 0.86980426
## 88 2466 76 4 1 0.6490284 0.83868455
## 89 2540 71 4 1 0.6398872 0.80639779
## 90 2583 67 3 1 0.6303366 0.77327040
## 91 2689 63 3 1 0.6203313 0.73918767
## 92 2769 60 2 1 0.6099924 0.70459493
## 93 2796 58 1 1 0.5994753 0.67001577
## 94 2847 56 1 1 0.5887704 0.63540858
## 95 3086 46 9 1 0.5759710 0.59475463
## 96 3090 45 0 1 0.5631717 0.55482841
## 97 3170 40 4 1 0.5490924 0.51167835
## 98 3244 39 0 1 0.5350131 0.46926140
## 99 3282 38 0 1 0.5209338 0.42750780
## 100 3358 35 2 1 0.5060500 0.38401699
## 101 3395 33 1 1 0.4907151 0.33982975
## 102 3428 31 1 1 0.4748856 0.29479889
## 103 3574 29 1 1 0.4585102 0.24875287
## 104 3584 26 2 1 0.4408752 0.19967929
## 105 3762 22 3 1 0.4208354 0.14443244
## 106 3839 19 2 1 0.3986862 0.08383758
## 107 3853 18 0 1 0.3765370 0.02353579
## 108 974 8 0 1 0.8750000 2.01341868
## 109 2256 7 0 1 0.7500000 1.24589932
## 110 2297 5 1 1 0.6000000 0.67172699
##
## Hasil transformasi minus log-log untuk variabel: Prothrombin_cat
## Time N_at_risk Censored Observed Survival MinusLogLog_S
## 1 51 245 0 1 0.9959184 5.49921391
## 2 71 244 0 1 0.9918367 4.80401545
## 3 110 243 0 1 0.9877551 4.39649200
## 4 198 242 0 1 0.9836735 4.10674450
## 5 264 241 0 1 0.9795918 3.88152837
## 6 321 240 0 1 0.9755102 3.69712703
## 7 334 239 0 1 0.9714286 3.54088930
## 8 348 238 0 1 0.9673469 3.40526356
## 9 460 237 0 1 0.9632653 3.28537879
## 10 515 236 0 1 0.9591837 3.17790913
## 11 611 234 1 1 0.9550846 3.08008483
## 12 673 233 0 1 0.9509855 2.99061658
## 13 694 232 0 1 0.9468864 2.90815920
## 14 733 230 1 1 0.9427695 2.83134717
## 15 750 228 1 1 0.9386346 2.75941163
## 16 762 227 0 1 0.9344996 2.69201885
## 17 769 226 0 1 0.9303647 2.62861121
## 18 786 225 0 1 0.9262297 2.56872770
## 19 790 223 1 1 0.9220762 2.51173485
## 20 797 222 0 1 0.9179227 2.45757884
## 21 799 221 0 1 0.9137692 2.40597856
## 22 853 218 2 1 0.9095776 2.35625048
## 23 859 217 0 1 0.9053860 2.30866490
## 24 890 215 1 1 0.9011749 2.26282725
## 25 904 213 1 1 0.8969441 2.21859540
## 26 930 212 0 1 0.8927132 2.17604135
## 27 943 210 1 1 0.8884622 2.13484273
## 28 974 209 0 1 0.8842112 2.09508844
## 29 980 208 0 1 0.8799602 2.05667354
## 30 999 206 1 1 0.8756885 2.01932652
## 31 1077 203 2 1 0.8713748 1.98280048
## 32 1080 202 0 1 0.8670610 1.94738989
## 33 1083 201 0 1 0.8627473 1.91302277
## 34 1152 198 2 1 0.8583900 1.87930135
## 35 1165 196 1 1 0.8540104 1.84635177
## 36 1191 195 0 2 0.8452514 1.78307001
## 37 1212 193 0 1 0.8408718 1.75263854
## 38 1235 188 4 1 0.8363991 1.72233014
## 39 1297 183 4 1 0.8318286 1.69211950
## 40 1360 176 6 1 0.8271023 1.66164283
## 41 1413 170 5 1 0.8222370 1.63103653
## 42 1427 167 2 1 0.8173134 1.60081214
## 43 1434 165 2 1 0.8123600 1.57112308
## 44 1444 162 1 1 0.8073455 1.54176229
## 45 1487 158 3 1 0.8022357 1.51252510
## 46 1536 155 2 1 0.7970600 1.48357483
## 47 1657 145 9 1 0.7915630 1.45352100
## 48 1682 143 1 1 0.7860276 1.42394061
## 49 1690 142 0 2 0.7749568 1.36669557
## 50 1741 137 3 1 0.7693002 1.33836527
## 51 1786 130 6 1 0.7633825 1.30934772
## 52 1827 127 2 1 0.7573716 1.28048929
## 53 1847 124 2 1 0.7512637 1.25176871
## 54 1925 120 3 1 0.7450032 1.22292883
## 55 2055 110 9 1 0.7382305 1.19237619
## 56 2090 109 0 1 0.7314577 1.16246020
## 57 2105 108 0 1 0.7246849 1.13314696
## 58 2224 101 7 1 0.7175098 1.10271485
## 59 2288 96 3 1 0.7100358 1.07165907
## 60 2297 94 1 1 0.7024822 1.04090428
## 61 2386 85 8 1 0.6942177 1.00794105
## 62 2400 84 0 1 0.6859532 0.97565373
## 63 2419 83 0 1 0.6776887 0.94400317
## 64 2466 79 3 1 0.6691104 0.91178530
## 65 2540 74 4 1 0.6600683 0.87848475
## 66 2583 69 4 1 0.6505021 0.84394521
## 67 2598 68 0 1 0.6409359 0.81007259
## 68 2689 63 4 1 0.6307623 0.77473449
## 69 2769 60 2 1 0.6202496 0.73891213
## 70 2796 58 1 1 0.6095557 0.70314706
## 71 2847 55 2 1 0.5984729 0.66675043
## 72 3086 45 9 1 0.5851735 0.62390666
## 73 3244 39 5 1 0.5701690 0.57656939
## 74 3358 35 3 1 0.5538785 0.52626081
## 75 3395 34 0 1 0.5375879 0.47696708
## 76 3428 32 1 1 0.5207883 0.42707954
## 77 3445 31 1 1 0.5039887 0.37804235
## 78 3574 29 0 1 0.4866098 0.32809748
## 79 3584 26 2 1 0.4678940 0.27507717
## 80 3839 20 5 1 0.4444993 0.20972547
## 81 3853 19 0 1 0.4211046 0.14517141
## 82 4079 13 5 1 0.3887119 0.05665847
## 83 4191 9 3 1 0.3455217 -0.06081262
## 84 41 31 0 1 0.9677419 3.41763709
## 85 77 30 0 1 0.9354839 2.70767965
## 86 131 29 0 1 0.9032258 2.28491519
## 87 140 28 0 1 0.8709677 1.97941278
## 88 179 27 0 1 0.8387097 1.73789269
## 89 186 26 0 1 0.8064516 1.53659934
## 90 191 25 0 1 0.7741935 1.36283813
## 91 216 24 0 1 0.7419355 1.20900884
## 92 223 23 0 1 0.7096774 1.07018592
## 93 304 22 0 1 0.6774194 0.94298188
## 94 326 21 0 1 0.6451613 0.82495450
## 95 388 20 0 1 0.6129032 0.71427230
## 96 400 19 0 1 0.5806452 0.60951318
## 97 549 18 0 1 0.5483871 0.50953669
## 98 552 17 0 1 0.5161290 0.41339877
## 99 597 16 0 1 0.4838710 0.32029204
## 100 850 15 0 1 0.4516129 0.22950138
## 101 1012 14 0 1 0.4193548 0.14036860
## 102 1170 13 0 1 0.3870968 0.05226160
## 103 1356 11 1 1 0.3519062 -0.04343369
## 104 1576 10 0 1 0.3167155 -0.13954561
## 105 2256 9 0 1 0.2815249 -0.23707351
## 106 3090 7 1 1 0.2413071 -0.35184276
## 107 3170 6 0 1 0.2010892 -0.47250458
## 108 3282 5 0 1 0.1608714 -0.60275742
## 109 3762 3 1 1 0.1072476 -0.80317363
##
## Hasil transformasi minus log-log untuk variabel: Age_cat
## Time N_at_risk Censored Observed Survival MinusLogLog_S
## 1 198 53 0 1 0.9811321 3.960782934
## 2 733 52 0 1 0.9622642 3.257973244
## 3 790 49 2 1 0.9426261 2.828768580
## 4 974 46 2 1 0.9221342 2.512510456
## 5 1212 44 1 1 0.9011766 2.262845351
## 6 1427 35 8 1 0.8754287 2.017093945
## 7 1434 34 1 1 0.8496808 1.814652570
## 8 1847 24 8 1 0.8142775 1.582532592
## 9 2105 16 7 1 0.7633851 1.309360622
## 10 2689 9 6 1 0.6785646 0.947328292
## 11 3244 7 1 1 0.5816268 0.612625183
## 12 3428 6 0 1 0.4846890 0.322621541
## 13 71 174 0 1 0.9942529 5.156174831
## 14 77 173 0 1 0.9885057 4.460133276
## 15 110 172 0 1 0.9827586 4.051759742
## 16 131 171 0 1 0.9770115 3.761155044
## 17 186 170 0 1 0.9712644 3.535074515
## 18 216 169 0 1 0.9655172 3.349801478
## 19 264 168 0 1 0.9597701 3.192684658
## 20 304 167 0 1 0.9540230 3.056172307
## 21 321 166 0 1 0.9482759 2.935393334
## 22 326 165 0 1 0.9425287 2.827021738
## 23 400 164 0 1 0.9367816 2.728685169
## 24 460 163 0 1 0.9310345 2.638631924
## 25 515 162 0 1 0.9252874 2.555531698
## 26 549 160 1 1 0.9195043 2.477884794
## 27 552 159 0 1 0.9137213 2.405396456
## 28 597 158 0 1 0.9079382 2.337394531
## 29 673 157 0 1 0.9021552 2.273329810
## 30 694 156 0 1 0.8963721 2.212747748
## 31 750 154 1 1 0.8905515 2.154904037
## 32 769 153 0 1 0.8847309 2.099875181
## 33 786 152 0 1 0.8789103 2.047381806
## 34 797 151 0 1 0.8730897 1.997183879
## 35 853 149 1 1 0.8672301 1.948757387
## 36 859 148 0 1 0.8613704 1.902262116
## 37 943 146 1 1 0.8554706 1.857235778
## 38 980 145 0 1 0.8495708 1.813857959
## 39 999 143 1 1 0.8436298 1.771712432
## 40 1077 141 1 1 0.8376466 1.730707517
## 41 1080 140 0 1 0.8316634 1.691041147
## 42 1083 139 0 1 0.8256802 1.652618102
## 43 1165 137 1 1 0.8196533 1.615084826
## 44 1170 136 0 1 0.8136265 1.578647309
## 45 1191 135 0 2 0.8015727 1.508780429
## 46 1297 129 4 1 0.7953590 1.474200547
## 47 1356 125 3 1 0.7889961 1.439721004
## 48 1413 121 3 1 0.7824755 1.405303199
## 49 1444 118 2 1 0.7758443 1.371195476
## 50 1536 115 2 1 0.7690979 1.337363094
## 51 1657 107 7 1 0.7619100 1.302222431
## 52 1690 105 1 2 0.7473975 1.233888707
## 53 1741 102 1 1 0.7400700 1.200610327
## 54 1827 97 4 1 0.7324405 1.166762996
## 55 1925 93 3 1 0.7245648 1.132632021
## 56 2055 88 4 1 0.7163311 1.097774120
## 57 2224 82 6 1 0.7075953 1.061654907
## 58 2256 79 1 1 0.6986384 1.025486486
## 59 2288 78 0 1 0.6896815 0.990138145
## 60 2297 76 1 1 0.6806068 0.955107993
## 61 2386 71 4 1 0.6710207 0.918906087
## 62 2400 70 0 1 0.6614347 0.883475194
## 63 2419 69 0 1 0.6518487 0.848765827
## 64 2466 64 4 1 0.6416636 0.812626637
## 65 2583 56 7 1 0.6302053 0.772819105
## 66 2598 55 0 1 0.6187470 0.733846760
## 67 2769 49 5 1 0.6061195 0.691791999
## 68 2847 45 3 1 0.5926502 0.647885317
## 69 3086 37 7 1 0.5766326 0.596837718
## 70 3170 32 4 1 0.5586129 0.540771941
## 71 3282 30 1 1 0.5399924 0.484183482
## 72 3358 28 1 1 0.5207070 0.426840277
## 73 3395 27 0 1 0.5014216 0.370617256
## 74 3445 25 1 1 0.4813647 0.313163864
## 75 3762 19 5 1 0.4560297 0.241820232
## 76 3839 17 1 1 0.4292044 0.167446414
## 77 3853 16 0 1 0.4023792 0.093914647
## 78 4079 11 4 1 0.3657992 -0.005654623
## 79 4191 9 1 1 0.3251549 -0.116407580
## 80 41 49 0 1 0.9795918 3.881528369
## 81 51 48 0 1 0.9591837 3.177909127
## 82 140 47 0 1 0.9387755 2.761784869
## 83 179 46 0 1 0.9183673 2.463249175
## 84 191 45 0 1 0.8979592 2.229049689
## 85 223 44 0 1 0.8775510 2.035461538
## 86 334 43 0 1 0.8571429 1.869824714
## 87 348 42 0 1 0.8367347 1.724578142
## 88 388 41 0 1 0.8163265 1.594840751
## 89 611 40 0 1 0.7959184 1.477275855
## 90 762 39 0 1 0.7755102 1.369499632
## 91 799 38 0 1 0.7551020 1.269748053
## 92 850 37 0 1 0.7346939 1.176677534
## 93 890 36 0 1 0.7142857 1.089239640
## 94 904 35 0 1 0.6938776 1.006599065
## 95 930 34 0 1 0.6734694 0.928078089
## 96 1012 32 1 1 0.6524235 0.850827400
## 97 1152 30 1 1 0.6306760 0.774437529
## 98 1235 28 1 1 0.6081519 0.698500221
## 99 1360 25 2 1 0.5838258 0.619613069
## 100 1487 23 1 1 0.5584421 0.540246903
## 101 1576 22 0 1 0.5330583 0.463426262
## 102 1682 20 1 1 0.5064054 0.385048408
## 103 1786 17 2 1 0.4766169 0.299697544
## 104 2090 15 1 1 0.4448424 0.210677586
## 105 2540 11 3 1 0.4044022 0.099438783
## 106 2796 10 0 1 0.3639620 -0.010648984
## 107 3090 7 2 1 0.3119674 -0.152597961
## 108 3574 3 3 1 0.2079783 -0.451280487
## 109 3584 2 0 1 0.1039891 -0.816898528variables = c("Drug", "Sex", "Ascites", "Spiders",
"Bilirubin_cat", "Cholesterol_cat", "Alk_Phos_cat",
"SGOT_cat", "Platelets_cat", "Prothrombin_cat", "Age_cat")
#simpan hasil transformasi kedalam satu dataframe
logtransf_results = data.frame()
for (var in variables) {
temp_df = results_list[[var]]
temp_df$Variable_type = var
logtransf_results = rbind(logtransf_results, temp_df)
}
print(logtransf_results)## Time N_at_risk Censored Observed Survival MinusLogLog_S Variable_type
## 1 41 136 0 1 0.99264706 4.908967102 Drug
## 2 71 135 0 1 0.98529412 4.212109308 Drug
## 3 131 134 0 1 0.97794118 3.802910449 Drug
## 4 140 133 0 1 0.97058824 3.511471176 Drug
## 5 179 132 0 1 0.96323529 3.284546653 Drug
## 6 198 131 0 1 0.95588235 3.098420025 Drug
## 7 223 130 0 1 0.94852941 2.940439840 Drug
## 8 334 129 0 1 0.94117647 2.803054168 Drug
## 9 348 128 0 1 0.93382353 2.681391728 Drug
## 10 388 127 0 1 0.92647059 2.572126326 Drug
## 11 400 126 0 1 0.91911765 2.472885414 Drug
## 12 515 125 0 1 0.91176471 2.381917085 Drug
## 13 673 123 1 1 0.90435198 2.297233133 Drug
## 14 694 122 0 1 0.89693926 2.218546105 Drug
## 15 750 119 2 1 0.88940196 2.143822221 Drug
## 16 762 118 0 1 0.88186465 2.073724410 Drug
## 17 799 117 0 1 0.87432735 2.007675959 Drug
## 18 904 113 3 1 0.86658994 1.943587194 Drug
## 19 980 111 1 1 0.85878282 1.882302213 Drug
## 20 999 110 0 1 0.85097571 1.824044889 Drug
## 21 1012 109 0 1 0.84316859 1.768501914 Drug
## 22 1077 108 0 1 0.83536147 1.715405578 Drug
## 23 1083 107 0 1 0.82755436 1.664525308 Drug
## 24 1152 106 0 1 0.81974724 1.615661084 Drug
## 25 1170 104 1 1 0.81186505 1.568194528 Drug
## 26 1191 103 0 2 0.79610068 1.478279778 Drug
## 27 1235 99 2 1 0.78805926 1.434720258 Drug
## 28 1297 96 2 1 0.77985031 1.391695786 Drug
## 29 1360 93 2 1 0.77146483 1.349136543 Drug
## 30 1434 88 4 1 0.76269818 1.306031754 Drug
## 31 1576 82 5 1 0.75339698 1.261732616 Drug
## 32 1657 78 3 1 0.74373805 1.217171548 Drug
## 33 1682 77 0 1 0.73407911 1.173965963 Drug
## 34 1690 76 0 2 0.71476124 1.091219516 Drug
## 35 1741 72 2 1 0.70483400 1.050413831 Drug
## 36 1827 67 4 1 0.69431409 1.008321481 Drug
## 37 1925 63 3 1 0.68329323 0.965399092 Drug
## 38 2055 57 5 1 0.67130563 0.919970595 Drug
## 39 2105 56 0 1 0.65931803 0.875750540 Drug
## 40 2224 54 1 1 0.64710844 0.831854463 Drug
## 41 2256 52 1 1 0.63466404 0.788206526 Drug
## 42 2288 50 1 1 0.62197076 0.744730640 Drug
## 43 2297 49 0 1 0.60927748 0.702225305 Drug
## 44 2386 43 5 1 0.59510824 0.655828321 Drug
## 45 2400 42 0 1 0.58093899 0.610444271 Drug
## 46 2540 37 4 1 0.56523794 0.561227207 Drug
## 47 2583 35 1 1 0.54908829 0.511665912 Drug
## 48 2598 34 0 1 0.53293863 0.463069324 Drug
## 49 2689 32 1 1 0.51628430 0.413853643 Drug
## 50 3086 22 9 1 0.49281683 0.345851280 Drug
## 51 3282 18 3 1 0.46543812 0.268172129 Drug
## 52 3574 15 2 1 0.43440891 0.181798892 Drug
## 53 3584 14 0 1 0.40337970 0.096646401 Drug
## 54 4079 6 7 1 0.33614975 -0.086359817 Drug
## 55 4191 5 0 1 0.26891980 -0.272575096 Drug
## 56 51 140 0 1 0.99285714 4.938060319 Drug
## 57 77 139 0 1 0.98571429 4.241309500 Drug
## 58 110 138 0 1 0.97857143 3.832218936 Drug
## 59 186 137 0 1 0.97142857 3.540889304 Drug
## 60 191 136 0 1 0.96428571 3.314075796 Drug
## 61 216 135 0 1 0.95714286 3.128061585 Drug
## 62 264 134 0 1 0.95000000 2.970195249 Drug
## 63 304 133 0 1 0.94285714 2.832924885 Drug
## 64 321 132 0 1 0.93571429 2.711379245 Drug
## 65 326 131 0 1 0.92857143 2.602232166 Drug
## 66 460 130 0 1 0.92142857 2.503111131 Drug
## 67 549 129 0 1 0.91428571 2.412264269 Drug
## 68 552 128 0 1 0.90714286 2.328361097 Drug
## 69 597 127 0 1 0.90000000 2.250367327 Drug
## 70 611 126 0 1 0.89285714 2.177462963 Drug
## 71 733 125 0 1 0.88571429 2.108986882 Drug
## 72 769 124 0 1 0.87857143 2.044398255 Drug
## 73 786 123 0 1 0.87142857 1.983249003 Drug
## 74 790 121 1 1 0.86422668 1.924695514 Drug
## 75 797 120 0 1 0.85702479 1.868931504 Drug
## 76 850 118 1 1 0.84976187 1.815238231 Drug
## 77 853 117 0 1 0.84249895 1.763855256 Drug
## 78 859 116 0 1 0.83523603 1.714571085 Drug
## 79 890 115 0 1 0.82797311 1.667201534 Drug
## 80 930 114 0 1 0.82071018 1.621585212 Drug
## 81 943 113 0 1 0.81344726 1.577579894 Drug
## 82 974 112 0 1 0.80618434 1.535059586 Drug
## 83 1080 108 3 1 0.79871967 1.492787757 Drug
## 84 1165 105 2 1 0.79111282 1.451090207 Drug
## 85 1212 104 0 1 0.78350596 1.410682927 Drug
## 86 1356 94 9 1 0.77517079 1.367779240 Drug
## 87 1413 92 1 1 0.76674502 1.325760218 Drug
## 88 1427 89 2 1 0.75812991 1.284096960 Drug
## 89 1444 86 2 1 0.74931445 1.242725543 Drug
## 90 1487 84 1 1 0.74039404 1.202065445 Drug
## 91 1536 82 1 1 0.73136484 1.162054303 Drug
## 92 1786 71 10 1 0.72106393 1.117711071 Drug
## 93 1847 68 2 1 0.71046005 1.073404978 Drug
## 94 2090 62 5 1 0.69900101 1.026934379 Drug
## 95 2419 50 11 1 0.68502099 0.972052428 Drug
## 96 2466 47 2 1 0.67044608 0.916760841 Drug
## 97 2769 37 9 1 0.65232591 0.850477302 Drug
## 98 2796 35 1 1 0.63368803 0.784827232 Drug
## 99 2847 32 2 1 0.61388528 0.717548055 Drug
## 100 3090 30 1 1 0.59342244 0.650377462 Drug
## 101 3170 27 2 1 0.57144383 0.580552533 Drug
## 102 3244 26 0 1 0.54946522 0.512811256 Drug
## 103 3358 24 1 1 0.52657083 0.444149674 Drug
## 104 3395 22 1 1 0.50263580 0.374127162 Drug
## 105 3428 20 1 1 0.47750401 0.302210126 Drug
## 106 3445 19 1 1 0.45237222 0.231616913 Drug
## 107 3762 13 4 1 0.41757435 0.135484549 Drug
## 108 3839 11 1 1 0.37961305 0.031900621 Drug
## 109 3853 10 0 1 0.34165174 -0.071355872 Drug
## 110 41 242 0 1 0.99586777 5.486868044 Sex
## 111 51 241 0 1 0.99173554 4.791644014 Sex
## 112 71 240 0 1 0.98760331 4.384094834 Sex
## 113 77 239 0 1 0.98347107 4.094321413 Sex
## 114 110 238 0 1 0.97933884 3.869079183 Sex
## 115 131 237 0 1 0.97520661 3.684651559 Sex
## 116 179 236 0 1 0.97107438 3.528387369 Sex
## 117 186 235 0 1 0.96694215 3.392734965 Sex
## 118 198 234 0 1 0.96280992 3.272823360 Sex
## 119 216 233 0 1 0.95867769 3.165326658 Sex
## 120 223 232 0 1 0.95454545 3.067872615 Sex
## 121 264 231 0 1 0.95041322 2.978709640 Sex
## 122 304 230 0 1 0.94628099 2.896507537 Sex
## 123 321 229 0 1 0.94214876 2.820232311 Sex
## 124 326 228 0 1 0.93801653 2.749064266 Sex
## 125 334 227 0 1 0.93388430 2.682342588 Sex
## 126 348 226 0 1 0.92975207 2.619526764 Sex
## 127 388 225 0 1 0.92561983 2.560169036 Sex
## 128 400 224 0 1 0.92148760 2.503894324 Sex
## 129 460 223 0 1 0.91735537 2.450385297 Sex
## 130 515 222 0 1 0.91322314 2.399371090 Sex
## 131 549 221 0 1 0.90909091 2.350618656 Sex
## 132 597 220 0 1 0.90495868 2.303926028 Sex
## 133 673 219 0 1 0.90082645 2.259117034 Sex
## 134 694 218 0 1 0.89669421 2.216037074 Sex
## 135 733 216 1 1 0.89254285 2.174361179 Sex
## 136 750 214 1 1 0.88837209 2.133985575 Sex
## 137 769 213 0 1 0.88420133 2.094997978 Sex
## 138 786 212 0 1 0.88003057 2.057299428 Sex
## 139 790 210 1 1 0.87583995 2.020630045 Sex
## 140 797 209 0 1 0.87164933 1.985091206 Sex
## 141 850 206 2 1 0.86741802 1.950279761 Sex
## 142 853 205 0 1 0.86318671 1.916477745 Sex
## 143 859 204 0 1 0.85895540 1.883622984 Sex
## 144 904 202 1 1 0.85470315 1.851502698 Sex
## 145 930 201 0 1 0.85045090 1.820229284 Sex
## 146 943 199 1 1 0.84617727 1.789603399 Sex
## 147 974 198 0 1 0.84190365 1.759739419 Sex
## 148 980 197 0 1 0.83763003 1.730596051 Sex
## 149 1080 193 3 1 0.83328998 1.701698016 Sex
## 150 1083 192 0 1 0.82894992 1.673467061 Sex
## 151 1165 188 3 1 0.82454062 1.645433668 Sex
## 152 1170 187 0 1 0.82013131 1.618020534 Sex
## 153 1191 186 0 2 0.81131269 1.564934366 Sex
## 154 1212 184 0 1 0.80690338 1.539206214 Sex
## 155 1235 179 4 1 0.80239554 1.513429807 Sex
## 156 1356 169 9 1 0.79764764 1.486829503 Sex
## 157 1413 163 5 1 0.79275410 1.459974494 Sex
## 158 1427 160 2 1 0.78779938 1.433336446 Sex
## 159 1434 158 2 1 0.78281331 1.407064417 Sex
## 160 1444 155 1 1 0.77776290 1.380974324 Sex
## 161 1487 151 3 1 0.77261216 1.354880596 Sex
## 162 1576 144 6 1 0.76724679 1.328226358 Sex
## 163 1657 139 4 1 0.76172703 1.301339381 Sex
## 164 1690 137 1 2 0.75060693 1.248715109 Sex
## 165 1741 132 3 1 0.74492051 1.222551796 Sex
## 166 1786 126 5 1 0.73900845 1.195852761 Sex
## 167 1827 123 2 1 0.73300025 1.169219618 Sex
## 168 1847 120 2 1 0.72689191 1.142634742 Sex
## 169 1925 116 3 1 0.72062560 1.115853407 Sex
## 170 2055 107 8 1 0.71389078 1.087597297 Sex
## 171 2090 106 0 1 0.70715596 1.059860809 Sex
## 172 2105 105 0 1 0.70042115 1.032618142 Sex
## 173 2224 98 6 1 0.69327399 1.004220744 Sex
## 174 2256 95 2 1 0.68597637 0.975743281 Sex
## 175 2288 93 1 1 0.67860028 0.947464076 Sex
## 176 2297 91 1 1 0.67114313 0.919363322 Sex
## 177 2400 82 8 1 0.66295846 0.889057586 Sex
## 178 2419 81 0 1 0.65477379 0.859283320 Sex
## 179 2466 76 4 1 0.64615834 0.828484341 Sex
## 180 2540 71 4 1 0.63705752 0.796520063 Sex
## 181 2583 66 4 1 0.62740514 0.763220357 Sex
## 182 2598 65 0 1 0.61775275 0.730502310 Sex
## 183 2769 58 6 1 0.60710184 0.695031503 Sex
## 184 2847 54 3 1 0.59585921 0.658261120 Sex
## 185 3086 45 8 1 0.58261790 0.615771939 Sex
## 186 3090 44 0 1 0.56937658 0.574096978 Sex
## 187 3170 38 5 1 0.55439299 0.527833644 Sex
## 188 3244 37 0 1 0.53940939 0.482431813 Sex
## 189 3282 35 1 1 0.52399770 0.436541014 Sex
## 190 3358 32 2 1 0.50762277 0.388583376 Sex
## 191 3428 29 2 1 0.49011853 0.338122385 Sex
## 192 3445 28 0 1 0.47261430 0.288381446 Sex
## 193 3574 27 0 1 0.45511007 0.239252632 Sex
## 194 3584 24 2 1 0.43614715 0.186599981 Sex
## 195 3762 20 3 1 0.41433979 0.126619461 Sex
## 196 3839 18 1 1 0.39132091 0.063763031 Sex
## 197 3853 17 0 1 0.36830204 0.001148734 Sex
## 198 140 34 0 1 0.97058824 3.511471176 Sex
## 199 191 33 0 1 0.94117647 2.803054168 Sex
## 200 552 31 1 1 0.91081594 2.370709292 Sex
## 201 611 30 0 1 0.88045541 2.061083123 Sex
## 202 762 29 0 1 0.85009488 1.817647802 Sex
## 203 799 28 0 1 0.81973435 1.615581947 Sex
## 204 890 26 1 1 0.78820610 1.435502784 Sex
## 205 999 25 0 1 0.75667786 1.277197231 Sex
## 206 1012 24 0 1 0.72514961 1.135139511 Sex
## 207 1077 23 0 1 0.69362137 1.005589145 Sex
## 208 1152 22 0 1 0.66209313 0.885885054 Sex
## 209 1297 21 0 1 0.63056488 0.774055276 Sex
## 210 1360 18 2 1 0.59553350 0.657205610 Sex
## 211 1536 17 0 1 0.56050212 0.546587076 Sex
## 212 1682 15 1 1 0.52313531 0.433995560 Sex
## 213 2386 11 3 1 0.47557755 0.296756034 Sex
## 214 2689 10 0 1 0.42801980 0.164184048 Sex
## 215 2796 9 0 1 0.38046204 0.034209672 Sex
## 216 3395 7 1 1 0.32611032 -0.113792455 Sex
## 217 4079 4 2 1 0.24458274 -0.342313440 Sex
## 218 4191 3 0 1 0.16305516 -0.595350610 Sex
## 219 71 257 0 1 0.99610895 5.547127398 Ascites
## 220 131 256 0 1 0.99221790 4.852025178 Ascites
## 221 140 255 0 1 0.98832685 4.444598632 Ascites
## 222 186 254 0 1 0.98443580 4.154948679 Ascites
## 223 198 253 0 1 0.98054475 3.929830757 Ascites
## 224 304 252 0 1 0.97665370 3.745528295 Ascites
## 225 321 251 0 1 0.97276265 3.589390127 Ascites
## 226 326 250 0 1 0.96887160 3.453864616 Ascites
## 227 460 249 0 1 0.96498054 3.334080785 Ascites
## 228 515 248 0 1 0.96108949 3.226712747 Ascites
## 229 552 246 1 1 0.95718263 3.129010586 Ascites
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## 588 786 199 0 1 0.90882480 2.347551627 Cholesterol_cat
## 589 790 197 1 1 0.90421147 2.295688757 Cholesterol_cat
## 590 797 196 0 1 0.89959815 2.246137486 Cholesterol_cat
## 591 799 195 0 1 0.89498482 2.198688994 Cholesterol_cat
## 592 850 192 2 1 0.89032344 2.152696673 Cholesterol_cat
## 593 853 191 0 1 0.88566207 2.108501175 Cholesterol_cat
## 594 859 190 0 1 0.88100069 2.065957863 Cholesterol_cat
## 595 890 188 1 1 0.87631451 2.024724442 Cholesterol_cat
## 596 904 186 1 1 0.87160314 1.984705572 Cholesterol_cat
## 597 930 185 0 1 0.86689178 1.946022201 Cholesterol_cat
## 598 943 183 1 1 0.86215466 1.908379118 Cholesterol_cat
## 599 974 182 0 1 0.85741755 1.871905506 Cholesterol_cat
## 600 980 181 0 1 0.85268044 1.836523986 Cholesterol_cat
## 601 999 179 1 1 0.84791686 1.801975311 Cholesterol_cat
## 602 1080 176 2 1 0.84309915 1.768019223 Cholesterol_cat
## 603 1083 175 0 1 0.83828144 1.734993258 Cholesterol_cat
## 604 1152 172 2 1 0.83340771 1.702472978 Cholesterol_cat
## 605 1165 170 1 1 0.82850531 1.670611264 Cholesterol_cat
## 606 1170 169 0 1 0.82360291 1.639553016 Cholesterol_cat
## 607 1191 168 0 2 0.81379812 1.579670595 Cholesterol_cat
## 608 1212 166 0 1 0.80889572 1.550766854 Cholesterol_cat
## 609 1235 162 3 1 0.80390253 1.521989266 Cholesterol_cat
## 610 1297 157 4 1 0.79878214 1.493135789 Cholesterol_cat
## 611 1356 150 6 1 0.79345692 1.463797501 Cholesterol_cat
## 612 1360 149 0 1 0.78813171 1.435106266 Cholesterol_cat
## 613 1413 143 5 1 0.78262030 1.406057841 Cholesterol_cat
## 614 1427 140 2 1 0.77703015 1.377231062 Cholesterol_cat
## 615 1434 139 1 1 0.77144001 1.349012558 Cholesterol_cat
## 616 1444 136 1 1 0.76576765 1.320969352 Cholesterol_cat
## 617 1487 134 1 1 0.76005297 1.293288103 Cholesterol_cat
## 618 1536 132 1 1 0.75429499 1.265948389 Cholesterol_cat
## 619 1657 123 8 1 0.74816251 1.237408737 Cholesterol_cat
## 620 1682 121 1 1 0.74197935 1.209206933 Cholesterol_cat
## 621 1690 120 0 2 0.72961303 1.154417910 Cholesterol_cat
## 622 1741 116 2 1 0.72332326 1.127323402 Cholesterol_cat
## 623 1786 109 6 1 0.71668727 1.099265402 Cholesterol_cat
## 624 1827 106 2 1 0.70992607 1.071207911 Cholesterol_cat
## 625 1847 104 1 1 0.70309986 1.043396088 Cholesterol_cat
## 626 1925 100 3 1 0.69606886 1.015264214 Cholesterol_cat
## 627 2090 92 7 1 0.68850289 0.985544992 Cholesterol_cat
## 628 2105 91 0 1 0.68093693 0.956369260 Cholesterol_cat
## 629 2224 87 4 1 0.67311006 0.926728972 Cholesterol_cat
## 630 2256 85 0 1 0.66519112 0.897270574 Cholesterol_cat
## 631 2288 84 0 1 0.65727218 0.868317405 Cholesterol_cat
## 632 2297 83 0 1 0.64935324 0.839842718 Cholesterol_cat
## 633 2386 78 4 1 0.64102820 0.810396233 Cholesterol_cat
## 634 2400 77 0 1 0.63270316 0.781423542 Cholesterol_cat
## 635 2419 76 0 1 0.62437811 0.752899006 Cholesterol_cat
## 636 2466 71 4 1 0.61558406 0.723227538 Cholesterol_cat
## 637 2540 67 3 1 0.60639623 0.692703970 Cholesterol_cat
## 638 2598 62 4 1 0.59661565 0.660714501 Cholesterol_cat
## 639 2689 59 2 1 0.58650352 0.628152622 Cholesterol_cat
## 640 2769 56 2 1 0.57603024 0.594940991 Cholesterol_cat
## 641 2796 55 0 1 0.56555697 0.562216725 Cholesterol_cat
## 642 2847 52 2 1 0.55468087 0.528714108 Cholesterol_cat
## 643 3086 43 8 1 0.54178132 0.489565177 Cholesterol_cat
## 644 3090 42 0 1 0.52888176 0.451000713 Cholesterol_cat
## 645 3244 36 5 1 0.51419060 0.407725764 Cholesterol_cat
## 646 3282 34 1 1 0.49906735 0.363822962 Cholesterol_cat
## 647 3358 31 2 1 0.48296840 0.317723428 Cholesterol_cat
## 648 3395 30 0 1 0.46686945 0.272195147 Cholesterol_cat
## 649 3428 28 1 1 0.45019554 0.225554898 Cholesterol_cat
## 650 3445 27 1 1 0.43352164 0.179349688 Cholesterol_cat
## 651 3574 25 0 1 0.41618077 0.131663916 Cholesterol_cat
## 652 3762 21 3 1 0.39636264 0.077501395 Cholesterol_cat
## 653 3839 18 2 1 0.37434249 0.017569293 Cholesterol_cat
## 654 3853 17 0 1 0.35232235 -0.042301318 Cholesterol_cat
## 655 4079 11 5 1 0.32029304 -0.129728250 Cholesterol_cat
## 656 4191 8 2 1 0.28025641 -0.240630041 Cholesterol_cat
## 657 41 140 0 1 0.99285714 4.938060319 Alk_Phos_cat
## 658 51 139 0 1 0.98571429 4.241309500 Alk_Phos_cat
## 659 110 138 0 1 0.97857143 3.832218936 Alk_Phos_cat
## 660 131 137 0 1 0.97142857 3.540889304 Alk_Phos_cat
## 661 140 136 0 1 0.96428571 3.314075796 Alk_Phos_cat
## 662 179 135 0 1 0.95714286 3.128061585 Alk_Phos_cat
## 663 186 134 0 1 0.95000000 2.970195249 Alk_Phos_cat
## 664 191 133 0 1 0.94285714 2.832924885 Alk_Phos_cat
## 665 304 132 0 1 0.93571429 2.711379245 Alk_Phos_cat
## 666 348 131 0 1 0.92857143 2.602232166 Alk_Phos_cat
## 667 515 130 0 1 0.92142857 2.503111131 Alk_Phos_cat
## 668 552 128 1 1 0.91422991 2.411583376 Alk_Phos_cat
## 669 750 126 1 1 0.90697412 2.326454050 Alk_Phos_cat
## 670 799 125 0 1 0.89971832 2.247400776 Alk_Phos_cat
## 671 850 122 2 1 0.89234358 2.172398973 Alk_Phos_cat
## 672 980 120 1 1 0.88490739 2.101504870 Alk_Phos_cat
## 673 1012 118 1 1 0.87740817 2.034216005 Alk_Phos_cat
## 674 1080 115 2 1 0.86977854 1.969571291 Alk_Phos_cat
## 675 1191 111 3 1 0.86194269 1.906722670 Alk_Phos_cat
## 676 1487 95 15 1 0.85286961 1.837916920 Alk_Phos_cat
## 677 1536 92 2 1 0.84359929 1.771500059 Alk_Phos_cat
## 678 1576 89 2 1 0.83412065 1.707176294 Alk_Phos_cat
## 679 1682 84 4 1 0.82419064 1.643235588 Alk_Phos_cat
## 680 1690 83 0 1 0.81426063 1.582431989 Alk_Phos_cat
## 681 1741 79 3 1 0.80395354 1.522279947 Alk_Phos_cat
## 682 1827 73 5 1 0.79294047 1.460987194 Alk_Phos_cat
## 683 2055 61 11 1 0.77994145 1.392165854 Alk_Phos_cat
## 684 2090 60 0 1 0.76694242 1.326729893 Alk_Phos_cat
## 685 2105 59 0 1 0.75394340 1.264296295 Alk_Phos_cat
## 686 2288 50 8 1 0.73886453 1.195209030 Alk_Phos_cat
## 687 2419 45 4 1 0.72244532 1.123580796 Alk_Phos_cat
## 688 2540 38 6 1 0.70343360 1.044744212 Alk_Phos_cat
## 689 2583 36 1 1 0.68389378 0.967708596 Alk_Phos_cat
## 690 2598 35 0 1 0.66435396 0.894186336 Alk_Phos_cat
## 691 2796 31 3 1 0.64292318 0.817056384 Alk_Phos_cat
## 692 3244 20 10 1 0.61077703 0.707198803 Alk_Phos_cat
## 693 3395 16 3 1 0.57260346 0.584181855 Alk_Phos_cat
## 694 3428 15 0 1 0.53442990 0.467519177 Alk_Phos_cat
## 695 3445 14 1 1 0.49625633 0.355728698 Alk_Phos_cat
## 696 3762 9 3 1 0.44111674 0.200348200 Alk_Phos_cat
## 697 71 136 0 1 0.99264706 4.908967102 Alk_Phos_cat
## 698 77 135 0 1 0.98529412 4.212109308 Alk_Phos_cat
## 699 198 134 0 1 0.97794118 3.802910449 Alk_Phos_cat
## 700 216 133 0 1 0.97058824 3.511471176 Alk_Phos_cat
## 701 223 132 0 1 0.96323529 3.284546653 Alk_Phos_cat
## 702 264 131 0 1 0.95588235 3.098420025 Alk_Phos_cat
## 703 321 130 0 1 0.94852941 2.940439840 Alk_Phos_cat
## 704 326 129 0 1 0.94117647 2.803054168 Alk_Phos_cat
## 705 334 128 0 1 0.93382353 2.681391728 Alk_Phos_cat
## 706 388 127 0 1 0.92647059 2.572126326 Alk_Phos_cat
## 707 400 126 0 1 0.91911765 2.472885414 Alk_Phos_cat
## 708 460 125 0 1 0.91176471 2.381917085 Alk_Phos_cat
## 709 549 124 0 1 0.90441176 2.297890825 Alk_Phos_cat
## 710 597 123 0 1 0.89705882 2.219772309 Alk_Phos_cat
## 711 611 122 0 1 0.88970588 2.146741503 Alk_Phos_cat
## 712 673 121 0 1 0.88235294 2.078137249 Alk_Phos_cat
## 713 694 120 0 1 0.87500000 2.013418678 Alk_Phos_cat
## 714 733 118 1 1 0.86758475 1.951631911 Alk_Phos_cat
## 715 762 117 0 1 0.86016949 1.892956491 Alk_Phos_cat
## 716 769 116 0 1 0.85275424 1.837067205 Alk_Phos_cat
## 717 786 115 0 1 0.84533898 1.783686729 Alk_Phos_cat
## 718 790 113 1 1 0.83785811 1.732133843 Alk_Phos_cat
## 719 797 112 0 1 0.83037723 1.682679873 Alk_Phos_cat
## 720 853 111 0 1 0.82289636 1.635140299 Alk_Phos_cat
## 721 859 110 0 1 0.81541548 1.589353437 Alk_Phos_cat
## 722 890 109 0 1 0.80793460 1.545176803 Alk_Phos_cat
## 723 904 107 1 1 0.80038381 1.502091816 Alk_Phos_cat
## 724 930 106 0 1 0.79283302 1.460403249 Alk_Phos_cat
## 725 943 104 1 1 0.78520963 1.419625501 Alk_Phos_cat
## 726 974 103 0 1 0.77758623 1.380070846 Alk_Phos_cat
## 727 999 102 0 1 0.76996284 1.341653644 Alk_Phos_cat
## 728 1077 101 0 1 0.76233944 1.304296553 Alk_Phos_cat
## 729 1083 100 0 1 0.75471605 1.267929473 Alk_Phos_cat
## 730 1152 99 0 1 0.74709266 1.232488661 Alk_Phos_cat
## 731 1165 98 0 1 0.73946926 1.197915967 Alk_Phos_cat
## 732 1170 97 0 1 0.73184587 1.164158190 Alk_Phos_cat
## 733 1191 96 0 1 0.72422247 1.131166518 Alk_Phos_cat
## 734 1212 95 0 1 0.71659908 1.098896045 Alk_Phos_cat
## 735 1235 92 2 1 0.70880996 1.066625857 Alk_Phos_cat
## 736 1297 89 2 1 0.70084580 1.034321772 Alk_Phos_cat
## 737 1356 85 3 1 0.69260056 1.001571303 Alk_Phos_cat
## 738 1360 84 0 1 0.68435531 0.969485739 Alk_Phos_cat
## 739 1413 81 2 1 0.67590648 0.937257619 Alk_Phos_cat
## 740 1427 79 1 1 0.66735070 0.905252919 Alk_Phos_cat
## 741 1434 78 1 1 0.65879492 0.873846886 Alk_Phos_cat
## 742 1444 75 1 1 0.65001099 0.842190244 Alk_Phos_cat
## 743 1657 70 4 1 0.64072512 0.809333317 Alk_Phos_cat
## 744 1690 68 1 1 0.63130269 0.776594378 Alk_Phos_cat
## 745 1786 65 2 1 0.62159034 0.743443043 Alk_Phos_cat
## 746 1847 63 1 1 0.61172383 0.710345512 Alk_Phos_cat
## 747 1925 61 1 1 0.60169557 0.677266627 Alk_Phos_cat
## 748 2224 58 2 1 0.59132151 0.643604152 Alk_Phos_cat
## 749 2256 55 2 1 0.58057021 0.609275736 Alk_Phos_cat
## 750 2297 53 1 1 0.56961605 0.574843860 Alk_Phos_cat
## 751 2386 48 4 1 0.55774905 0.538117780 Alk_Phos_cat
## 752 2400 47 0 1 0.54588205 0.501944593 Alk_Phos_cat
## 753 2466 45 1 1 0.53375134 0.465493485 Alk_Phos_cat
## 754 2689 38 6 1 0.51970525 0.423893696 Alk_Phos_cat
## 755 2769 35 2 1 0.50485653 0.380556435 Alk_Phos_cat
## 756 2847 33 1 1 0.48955785 0.336518529 Alk_Phos_cat
## 757 3086 30 2 1 0.47323925 0.290146171 Alk_Phos_cat
## 758 3090 29 0 1 0.45692066 0.244309068 Alk_Phos_cat
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## 763 3584 18 1 1 0.36001886 -0.021368903 Alk_Phos_cat
## 764 3839 14 3 1 0.33430323 -0.091399658 Alk_Phos_cat
## 765 3853 13 0 1 0.30858760 -0.161905845 Alk_Phos_cat
## 766 4079 9 3 1 0.27430009 -0.257376901 Alk_Phos_cat
## 767 4191 8 0 1 0.24001257 -0.355619160 Alk_Phos_cat
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## 769 2466 4 1 1 0.62500000 0.755014863 SGOT_cat
## 770 3090 1 2 1 0.00000000 -Inf SGOT_cat
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## 774 77 267 0 1 0.98518519 4.204674055 SGOT_cat
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## 781 216 260 0 1 0.95925926 3.179801822 SGOT_cat
## 782 223 259 0 1 0.95555556 3.090870238 SGOT_cat
## 783 264 258 0 1 0.95185185 3.008901121 SGOT_cat
## 784 304 257 0 1 0.94814815 2.932860493 SGOT_cat
## 785 321 256 0 1 0.94444444 2.861928676 SGOT_cat
## 786 326 255 0 1 0.94074074 2.795444874 SGOT_cat
## 787 334 254 0 1 0.93703704 2.732868591 SGOT_cat
## 788 348 253 0 1 0.93333333 2.673752092 SGOT_cat
## 789 388 252 0 1 0.92962963 2.617720313 SGOT_cat
## 790 400 251 0 1 0.92592593 2.564455944 SGOT_cat
## 791 460 250 0 1 0.92222222 2.513688141 SGOT_cat
## 792 515 249 0 1 0.91851852 2.465183875 SGOT_cat
## 793 549 247 1 1 0.91479982 2.418557117 SGOT_cat
## 794 552 246 0 1 0.91108112 2.373830440 SGOT_cat
## 795 597 245 0 1 0.90736242 2.330847497 SGOT_cat
## 796 611 244 0 1 0.90364372 2.289470406 SGOT_cat
## 797 673 243 0 1 0.89992503 2.249576948 SGOT_cat
## 798 694 242 0 1 0.89620633 2.211058280 SGOT_cat
## 799 733 240 1 1 0.89247213 2.173664424 SGOT_cat
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## 802 769 236 0 1 0.88122249 2.067946686 SGOT_cat
## 803 786 235 0 1 0.87747260 2.034777626 SGOT_cat
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## 805 797 232 0 1 0.86994065 1.970907987 SGOT_cat
## 806 799 231 0 1 0.86617467 1.940245392 SGOT_cat
## 807 850 228 2 1 0.86237566 1.910108628 SGOT_cat
## 808 853 227 0 1 0.85857665 1.880726299 SGOT_cat
## 809 859 226 0 1 0.85477764 1.852057923 SGOT_cat
## 810 890 224 1 1 0.85096167 1.823942652 SGOT_cat
## 811 904 222 1 1 0.84712851 1.796352735 SGOT_cat
## 812 930 221 0 1 0.84329534 1.769383491 SGOT_cat
## 813 943 219 1 1 0.83944468 1.742885308 SGOT_cat
## 814 974 218 0 1 0.83559402 1.716954032 SGOT_cat
## 815 980 217 0 1 0.83174335 1.691562946 SGOT_cat
## 816 999 215 1 1 0.82787478 1.666572612 SGOT_cat
## 817 1012 214 0 1 0.82400621 1.642078789 SGOT_cat
## 818 1077 211 2 1 0.82010096 1.617833959 SGOT_cat
## 819 1080 210 0 1 0.81619572 1.594051399 SGOT_cat
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## 822 1165 204 1 1 0.80438483 1.524740781 SGOT_cat
## 823 1170 203 0 1 0.80042235 1.502308055 SGOT_cat
## 824 1191 202 0 2 0.79249737 1.458580844 SGOT_cat
## 825 1212 200 0 1 0.78853489 1.437256636 SGOT_cat
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## 831 1427 173 2 1 0.76305334 1.307751804 SGOT_cat
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## 839 1690 147 0 2 0.72021333 1.114108273 SGOT_cat
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## 874 3853 21 0 1 0.37724951 0.025473214 SGOT_cat
## 875 4079 14 6 1 0.35030311 -0.047795826 SGOT_cat
## 876 4191 10 3 1 0.31527280 -0.143508809 SGOT_cat
## 877 41 32 0 1 0.96875000 3.449903552 Platelets_cat
## 878 77 31 0 1 0.93750000 2.740493007 Platelets_cat
## 879 110 30 0 1 0.90625000 2.318307314 Platelets_cat
## 880 140 29 0 1 0.87500000 2.013418678 Platelets_cat
## 881 223 28 0 1 0.84375000 1.772550920 Platelets_cat
## 882 304 27 0 1 0.81250000 1.571952527 Platelets_cat
## 883 321 26 0 1 0.78125000 1.398933589 Platelets_cat
## 884 326 25 0 1 0.75000000 1.245899324 Platelets_cat
## 885 348 24 0 1 0.71875000 1.107930508 Platelets_cat
## 886 388 23 0 1 0.68750000 0.981647055 Platelets_cat
## 887 549 22 0 1 0.65625000 0.864615531 Platelets_cat
## 888 552 21 0 1 0.62500000 0.755014863 Platelets_cat
## 889 762 20 0 1 0.59375000 0.651435489 Platelets_cat
## 890 850 19 0 1 0.56250000 0.552752143 Platelets_cat
## 891 1191 18 0 1 0.53125000 0.458039393 Platelets_cat
## 892 2105 11 6 1 0.48295455 0.317684012 Platelets_cat
## 893 2598 6 4 1 0.40246212 0.094141139 Platelets_cat
## 894 3445 4 1 1 0.30184659 -0.180516903 Platelets_cat
## 895 4079 2 1 1 0.15092330 -0.637097090 Platelets_cat
## 896 4191 1 0 1 0.00000000 -Inf Platelets_cat
## 897 51 236 0 1 0.99576271 5.461709411 Platelets_cat
## 898 71 235 0 1 0.99152542 4.766432298 Platelets_cat
## 899 131 234 0 1 0.98728814 4.358829660 Platelets_cat
## 900 179 233 0 1 0.98305085 4.069002403 Platelets_cat
## 901 186 232 0 1 0.97881356 3.843705952 Platelets_cat
## 902 191 231 0 1 0.97457627 3.659223721 Platelets_cat
## 903 198 230 0 1 0.97033898 3.502904530 Platelets_cat
## 904 216 229 0 1 0.96610169 3.367196729 Platelets_cat
## 905 264 228 0 1 0.96186441 3.247229325 Platelets_cat
## 906 334 227 0 1 0.95762712 3.139676417 Platelets_cat
## 907 400 226 0 1 0.95338983 3.042165758 Platelets_cat
## 908 460 225 0 1 0.94915254 2.952945749 Platelets_cat
## 909 515 224 0 1 0.94491525 2.870686192 Platelets_cat
## 910 597 222 1 1 0.94065888 2.794021346 Platelets_cat
## 911 611 221 0 1 0.93640250 2.722506150 Platelets_cat
## 912 673 220 0 1 0.93214613 2.655471713 Platelets_cat
## 913 694 219 0 1 0.92788975 2.592371342 Platelets_cat
## 914 733 217 1 1 0.92361376 2.532485187 Platelets_cat
## 915 750 215 1 1 0.91931789 2.475471561 Platelets_cat
## 916 769 214 0 1 0.91502201 2.421287991 Platelets_cat
## 917 786 213 0 1 0.91072613 2.369654259 Platelets_cat
## 918 790 211 1 1 0.90640989 2.320101059 Platelets_cat
## 919 797 210 0 1 0.90209366 2.272667778 Platelets_cat
## 920 799 209 0 1 0.89777742 2.227170564 Platelets_cat
## 921 853 206 2 1 0.89341928 2.183032110 Platelets_cat
## 922 859 205 0 1 0.88906113 2.140557398 Platelets_cat
## 923 890 203 1 1 0.88468152 2.099419276 Platelets_cat
## 924 904 201 1 1 0.88028012 2.059520460 Platelets_cat
## 925 930 200 0 1 0.87587872 2.020964009 Platelets_cat
## 926 943 198 1 1 0.87145509 1.983470143 Platelets_cat
## 927 980 197 0 1 0.86703146 1.947150799 Platelets_cat
## 928 999 195 1 1 0.86258514 1.911750378 Platelets_cat
## 929 1012 194 0 1 0.85813883 1.877386727 Platelets_cat
## 930 1077 191 2 1 0.85364596 1.843650357 Platelets_cat
## 931 1080 190 0 1 0.84915308 1.810845659 Platelets_cat
## 932 1083 189 0 1 0.84466021 1.778917174 Platelets_cat
## 933 1152 186 2 1 0.84011903 1.747484049 Platelets_cat
## 934 1165 184 1 1 0.83555316 1.716681837 Platelets_cat
## 935 1170 183 0 1 0.83098730 1.686638818 Platelets_cat
## 936 1191 182 0 1 0.82642143 1.657313753 Platelets_cat
## 937 1212 181 0 1 0.82185557 1.628668606 Platelets_cat
## 938 1235 176 4 1 0.81718593 1.600039078 Platelets_cat
## 939 1297 171 4 1 0.81240707 1.571401805 Platelets_cat
## 940 1356 164 6 1 0.80745337 1.542387042 Platelets_cat
## 941 1360 163 0 1 0.80249967 1.514019373 Platelets_cat
## 942 1413 158 4 1 0.79742055 1.485570897 Platelets_cat
## 943 1427 155 2 1 0.79227591 1.457379780 Platelets_cat
## 944 1434 153 2 1 0.78709763 1.429607025 Platelets_cat
## 945 1444 150 1 1 0.78185031 1.402049945 Platelets_cat
## 946 1487 147 2 1 0.77653160 1.374690205 Platelets_cat
## 947 1536 145 1 1 0.77117621 1.347695459 Platelets_cat
## 948 1576 140 4 1 0.76566781 1.320480895 Platelets_cat
## 949 1657 134 5 1 0.75995387 1.292812978 Platelets_cat
## 950 1682 132 1 1 0.75419665 1.265486075 Platelets_cat
## 951 1690 131 0 2 0.74268219 1.212384555 Platelets_cat
## 952 1741 126 3 1 0.73678789 1.185952063 Platelets_cat
## 953 1786 120 5 1 0.73064799 1.158924623 Platelets_cat
## 954 1827 117 2 1 0.72440314 1.131939869 Platelets_cat
## 955 1847 114 2 1 0.71804872 1.104978959 Platelets_cat
## 956 1925 110 3 1 0.71152101 1.077779795 Platelets_cat
## 957 2055 101 8 1 0.70447625 1.048963448 Platelets_cat
## 958 2090 100 0 1 0.69743148 1.020676718 Platelets_cat
## 959 2224 94 6 1 0.69001200 0.991428451 Platelets_cat
## 960 2288 90 2 1 0.68234520 0.961759989 Platelets_cat
## 961 2386 83 6 1 0.67412417 0.930539384 Platelets_cat
## 962 2400 82 0 1 0.66590315 0.899898215 Platelets_cat
## 963 2419 81 0 1 0.65768212 0.869804256 Platelets_cat
## 964 2466 76 4 1 0.64902841 0.838684549 Platelets_cat
## 965 2540 71 4 1 0.63988716 0.806397794 Platelets_cat
## 966 2583 67 3 1 0.63033661 0.773270400 Platelets_cat
## 967 2689 63 3 1 0.62033127 0.739187674 Platelets_cat
## 968 2769 60 2 1 0.60999241 0.704594930 Platelets_cat
## 969 2796 58 1 1 0.59947530 0.670015774 Platelets_cat
## 970 2847 56 1 1 0.58877038 0.635408580 Platelets_cat
## 971 3086 46 9 1 0.57597103 0.594754632 Platelets_cat
## 972 3090 45 0 1 0.56317167 0.554828411 Platelets_cat
## 973 3170 40 4 1 0.54909238 0.511678350 Platelets_cat
## 974 3244 39 0 1 0.53501309 0.469261395 Platelets_cat
## 975 3282 38 0 1 0.52093380 0.427507800 Platelets_cat
## 976 3358 35 2 1 0.50604997 0.384016994 Platelets_cat
## 977 3395 33 1 1 0.49071513 0.339829751 Platelets_cat
## 978 3428 31 1 1 0.47488561 0.294798892 Platelets_cat
## 979 3574 29 1 1 0.45851024 0.248752871 Platelets_cat
## 980 3584 26 2 1 0.44087523 0.199679294 Platelets_cat
## 981 3762 22 3 1 0.42083545 0.144432442 Platelets_cat
## 982 3839 19 2 1 0.39868621 0.083837578 Platelets_cat
## 983 3853 18 0 1 0.37653698 0.023535789 Platelets_cat
## 984 974 8 0 1 0.87500000 2.013418678 Platelets_cat
## 985 2256 7 0 1 0.75000000 1.245899324 Platelets_cat
## 986 2297 5 1 1 0.60000000 0.671726992 Platelets_cat
## 987 51 245 0 1 0.99591837 5.499213915 Prothrombin_cat
## 988 71 244 0 1 0.99183673 4.804015446 Prothrombin_cat
## 989 110 243 0 1 0.98775510 4.396492004 Prothrombin_cat
## 990 198 242 0 1 0.98367347 4.106744501 Prothrombin_cat
## 991 264 241 0 1 0.97959184 3.881528369 Prothrombin_cat
## 992 321 240 0 1 0.97551020 3.697127028 Prothrombin_cat
## 993 334 239 0 1 0.97142857 3.540889304 Prothrombin_cat
## 994 348 238 0 1 0.96734694 3.405263555 Prothrombin_cat
## 995 460 237 0 1 0.96326531 3.285378794 Prothrombin_cat
## 996 515 236 0 1 0.95918367 3.177909127 Prothrombin_cat
## 997 611 234 1 1 0.95508460 3.080084828 Prothrombin_cat
## 998 673 233 0 1 0.95098552 2.990616579 Prothrombin_cat
## 999 694 232 0 1 0.94688645 2.908159198 Prothrombin_cat
## 1000 733 230 1 1 0.94276955 2.831347174 Prothrombin_cat
## 1001 750 228 1 1 0.93863460 2.759411633 Prothrombin_cat
## 1002 762 227 0 1 0.93449964 2.692018848 Prothrombin_cat
## 1003 769 226 0 1 0.93036469 2.628611214 Prothrombin_cat
## 1004 786 225 0 1 0.92622973 2.568727698 Prothrombin_cat
## 1005 790 223 1 1 0.92207624 2.511734850 Prothrombin_cat
## 1006 797 222 0 1 0.91792274 2.457578836 Prothrombin_cat
## 1007 799 221 0 1 0.91376924 2.405978558 Prothrombin_cat
## 1008 853 218 2 1 0.90957764 2.356250482 Prothrombin_cat
## 1009 859 217 0 1 0.90538604 2.308664898 Prothrombin_cat
## 1010 890 215 1 1 0.90117494 2.262827245 Prothrombin_cat
## 1011 904 213 1 1 0.89694407 2.218595403 Prothrombin_cat
## 1012 930 212 0 1 0.89271320 2.176041351 Prothrombin_cat
## 1013 943 210 1 1 0.88846219 2.134842731 Prothrombin_cat
## 1014 974 209 0 1 0.88421117 2.095088436 Prothrombin_cat
## 1015 980 208 0 1 0.87996016 2.056673537 Prothrombin_cat
## 1016 999 206 1 1 0.87568851 2.019326517 Prothrombin_cat
## 1017 1077 203 2 1 0.87137477 1.982800481 Prothrombin_cat
## 1018 1080 202 0 1 0.86706103 1.947389892 Prothrombin_cat
## 1019 1083 201 0 1 0.86274730 1.913022774 Prothrombin_cat
## 1020 1152 198 2 1 0.85838999 1.879301345 Prothrombin_cat
## 1021 1165 196 1 1 0.85401045 1.846351769 Prothrombin_cat
## 1022 1191 195 0 2 0.84525137 1.783070005 Prothrombin_cat
## 1023 1212 193 0 1 0.84087183 1.752638542 Prothrombin_cat
## 1024 1235 188 4 1 0.83639910 1.722330143 Prothrombin_cat
## 1025 1297 183 4 1 0.83182862 1.692119496 Prothrombin_cat
## 1026 1360 176 6 1 0.82710232 1.661642828 Prothrombin_cat
## 1027 1413 170 5 1 0.82223701 1.631036527 Prothrombin_cat
## 1028 1427 167 2 1 0.81731343 1.600812136 Prothrombin_cat
## 1029 1434 165 2 1 0.81236002 1.571123075 Prothrombin_cat
## 1030 1444 162 1 1 0.80734545 1.541762286 Prothrombin_cat
## 1031 1487 158 3 1 0.80223567 1.512525104 Prothrombin_cat
## 1032 1536 155 2 1 0.79705996 1.483574828 Prothrombin_cat
## 1033 1657 145 9 1 0.79156299 1.453521000 Prothrombin_cat
## 1034 1682 143 1 1 0.78602759 1.423940608 Prothrombin_cat
## 1035 1690 142 0 2 0.77495677 1.366695574 Prothrombin_cat
## 1036 1741 137 3 1 0.76930016 1.338365268 Prothrombin_cat
## 1037 1786 130 6 1 0.76338246 1.309347719 Prothrombin_cat
## 1038 1827 127 2 1 0.75737158 1.280489294 Prothrombin_cat
## 1039 1847 124 2 1 0.75126374 1.251768706 Prothrombin_cat
## 1040 1925 120 3 1 0.74500321 1.222928834 Prothrombin_cat
## 1041 2055 110 9 1 0.73823045 1.192376188 Prothrombin_cat
## 1042 2090 109 0 1 0.73145770 1.162460199 Prothrombin_cat
## 1043 2105 108 0 1 0.72468494 1.133146956 Prothrombin_cat
## 1044 2224 101 7 1 0.71750984 1.102714851 Prothrombin_cat
## 1045 2288 96 3 1 0.71003578 1.071659071 Prothrombin_cat
## 1046 2297 94 1 1 0.70248221 1.040904285 Prothrombin_cat
## 1047 2386 85 8 1 0.69421771 1.007941051 Prothrombin_cat
## 1048 2400 84 0 1 0.68595322 0.975653732 Prothrombin_cat
## 1049 2419 83 0 1 0.67768872 0.944003169 Prothrombin_cat
## 1050 2466 79 3 1 0.66911038 0.911785302 Prothrombin_cat
## 1051 2540 74 4 1 0.66006835 0.878484745 Prothrombin_cat
## 1052 2583 69 4 1 0.65050214 0.843945212 Prothrombin_cat
## 1053 2598 68 0 1 0.64093593 0.810072590 Prothrombin_cat
## 1054 2689 63 4 1 0.63076235 0.774734495 Prothrombin_cat
## 1055 2769 60 2 1 0.62024964 0.738912132 Prothrombin_cat
## 1056 2796 58 1 1 0.60955568 0.703147058 Prothrombin_cat
## 1057 2847 55 2 1 0.59847285 0.666750428 Prothrombin_cat
## 1058 3086 45 9 1 0.58517345 0.623906659 Prothrombin_cat
## 1059 3244 39 5 1 0.57016901 0.576569389 Prothrombin_cat
## 1060 3358 35 3 1 0.55387846 0.526260810 Prothrombin_cat
## 1061 3395 34 0 1 0.53758792 0.476967084 Prothrombin_cat
## 1062 3428 32 1 1 0.52078830 0.427079541 Prothrombin_cat
## 1063 3445 31 1 1 0.50398868 0.378042353 Prothrombin_cat
## 1064 3574 29 0 1 0.48660976 0.328097484 Prothrombin_cat
## 1065 3584 26 2 1 0.46789400 0.275077166 Prothrombin_cat
## 1066 3839 20 5 1 0.44449930 0.209725469 Prothrombin_cat
## 1067 3853 19 0 1 0.42110460 0.145171415 Prothrombin_cat
## 1068 4079 13 5 1 0.38871194 0.056658466 Prothrombin_cat
## 1069 4191 9 3 1 0.34552172 -0.060812624 Prothrombin_cat
## 1070 41 31 0 1 0.96774194 3.417637092 Prothrombin_cat
## 1071 77 30 0 1 0.93548387 2.707679652 Prothrombin_cat
## 1072 131 29 0 1 0.90322581 2.284915186 Prothrombin_cat
## 1073 140 28 0 1 0.87096774 1.979412778 Prothrombin_cat
## 1074 179 27 0 1 0.83870968 1.737892690 Prothrombin_cat
## 1075 186 26 0 1 0.80645161 1.536599340 Prothrombin_cat
## 1076 191 25 0 1 0.77419355 1.362838126 Prothrombin_cat
## 1077 216 24 0 1 0.74193548 1.209008835 Prothrombin_cat
## 1078 223 23 0 1 0.70967742 1.070185920 Prothrombin_cat
## 1079 304 22 0 1 0.67741935 0.942981875 Prothrombin_cat
## 1080 326 21 0 1 0.64516129 0.824954504 Prothrombin_cat
## 1081 388 20 0 1 0.61290323 0.714272302 Prothrombin_cat
## 1082 400 19 0 1 0.58064516 0.609513182 Prothrombin_cat
## 1083 549 18 0 1 0.54838710 0.509536687 Prothrombin_cat
## 1084 552 17 0 1 0.51612903 0.413398773 Prothrombin_cat
## 1085 597 16 0 1 0.48387097 0.320292040 Prothrombin_cat
## 1086 850 15 0 1 0.45161290 0.229501376 Prothrombin_cat
## 1087 1012 14 0 1 0.41935484 0.140368602 Prothrombin_cat
## 1088 1170 13 0 1 0.38709677 0.052261600 Prothrombin_cat
## 1089 1356 11 1 1 0.35190616 -0.043433686 Prothrombin_cat
## 1090 1576 10 0 1 0.31671554 -0.139545615 Prothrombin_cat
## 1091 2256 9 0 1 0.28152493 -0.237073506 Prothrombin_cat
## 1092 3090 7 1 1 0.24130708 -0.351842764 Prothrombin_cat
## 1093 3170 6 0 1 0.20108923 -0.472504576 Prothrombin_cat
## 1094 3282 5 0 1 0.16087139 -0.602757416 Prothrombin_cat
## 1095 3762 3 1 1 0.10724759 -0.803173626 Prothrombin_cat
## 1096 198 53 0 1 0.98113208 3.960782934 Age_cat
## 1097 733 52 0 1 0.96226415 3.257973244 Age_cat
## 1098 790 49 2 1 0.94262611 2.828768580 Age_cat
## 1099 974 46 2 1 0.92213424 2.512510456 Age_cat
## 1100 1212 44 1 1 0.90117664 2.262845351 Age_cat
## 1101 1427 35 8 1 0.87542873 2.017093945 Age_cat
## 1102 1434 34 1 1 0.84968083 1.814652570 Age_cat
## 1103 1847 24 8 1 0.81427746 1.582532592 Age_cat
## 1104 2105 16 7 1 0.76338512 1.309360622 Age_cat
## 1105 2689 9 6 1 0.67856455 0.947328292 Age_cat
## 1106 3244 7 1 1 0.58162676 0.612625183 Age_cat
## 1107 3428 6 0 1 0.48468897 0.322621541 Age_cat
## 1108 71 174 0 1 0.99425287 5.156174831 Age_cat
## 1109 77 173 0 1 0.98850575 4.460133276 Age_cat
## 1110 110 172 0 1 0.98275862 4.051759742 Age_cat
## 1111 131 171 0 1 0.97701149 3.761155044 Age_cat
## 1112 186 170 0 1 0.97126437 3.535074515 Age_cat
## 1113 216 169 0 1 0.96551724 3.349801478 Age_cat
## 1114 264 168 0 1 0.95977011 3.192684658 Age_cat
## 1115 304 167 0 1 0.95402299 3.056172307 Age_cat
## 1116 321 166 0 1 0.94827586 2.935393334 Age_cat
## 1117 326 165 0 1 0.94252874 2.827021738 Age_cat
## 1118 400 164 0 1 0.93678161 2.728685169 Age_cat
## 1119 460 163 0 1 0.93103448 2.638631924 Age_cat
## 1120 515 162 0 1 0.92528736 2.555531698 Age_cat
## 1121 549 160 1 1 0.91950431 2.477884794 Age_cat
## 1122 552 159 0 1 0.91372126 2.405396456 Age_cat
## 1123 597 158 0 1 0.90793822 2.337394531 Age_cat
## 1124 673 157 0 1 0.90215517 2.273329810 Age_cat
## 1125 694 156 0 1 0.89637213 2.212747748 Age_cat
## 1126 750 154 1 1 0.89055153 2.154904037 Age_cat
## 1127 769 153 0 1 0.88473093 2.099875181 Age_cat
## 1128 786 152 0 1 0.87891033 2.047381806 Age_cat
## 1129 797 151 0 1 0.87308973 1.997183879 Age_cat
## 1130 853 149 1 1 0.86723007 1.948757387 Age_cat
## 1131 859 148 0 1 0.86137041 1.902262116 Age_cat
## 1132 943 146 1 1 0.85547061 1.857235778 Age_cat
## 1133 980 145 0 1 0.84957081 1.813857959 Age_cat
## 1134 999 143 1 1 0.84362976 1.771712432 Age_cat
## 1135 1077 141 1 1 0.83764657 1.730707517 Age_cat
## 1136 1080 140 0 1 0.83166338 1.691041147 Age_cat
## 1137 1083 139 0 1 0.82568019 1.652618102 Age_cat
## 1138 1165 137 1 1 0.81965333 1.615084826 Age_cat
## 1139 1170 136 0 1 0.81362646 1.578647309 Age_cat
## 1140 1191 135 0 2 0.80157274 1.508780429 Age_cat
## 1141 1297 129 4 1 0.79535900 1.474200547 Age_cat
## 1142 1356 125 3 1 0.78899612 1.439721004 Age_cat
## 1143 1413 121 3 1 0.78247550 1.405303199 Age_cat
## 1144 1444 118 2 1 0.77584435 1.371195476 Age_cat
## 1145 1536 115 2 1 0.76909787 1.337363094 Age_cat
## 1146 1657 107 7 1 0.76191004 1.302222431 Age_cat
## 1147 1690 105 1 2 0.74739747 1.233888707 Age_cat
## 1148 1741 102 1 1 0.74007005 1.200610327 Age_cat
## 1149 1827 97 4 1 0.73244046 1.166762996 Age_cat
## 1150 1925 93 3 1 0.72456475 1.132632021 Age_cat
## 1151 2055 88 4 1 0.71633106 1.097774120 Age_cat
## 1152 2224 82 6 1 0.70759532 1.061654907 Age_cat
## 1153 2256 79 1 1 0.69863842 1.025486486 Age_cat
## 1154 2288 78 0 1 0.68968151 0.990138145 Age_cat
## 1155 2297 76 1 1 0.68060676 0.955107993 Age_cat
## 1156 2386 71 4 1 0.67102075 0.918906087 Age_cat
## 1157 2400 70 0 1 0.66143474 0.883475194 Age_cat
## 1158 2419 69 0 1 0.65184872 0.848765827 Age_cat
## 1159 2466 64 4 1 0.64166359 0.812626637 Age_cat
## 1160 2583 56 7 1 0.63020531 0.772819105 Age_cat
## 1161 2598 55 0 1 0.61874703 0.733846760 Age_cat
## 1162 2769 49 5 1 0.60611954 0.691791999 Age_cat
## 1163 2847 45 3 1 0.59265022 0.647885317 Age_cat
## 1164 3086 37 7 1 0.57663264 0.596837718 Age_cat
## 1165 3170 32 4 1 0.55861287 0.540771941 Age_cat
## 1166 3282 30 1 1 0.53999245 0.484183482 Age_cat
## 1167 3358 28 1 1 0.52070700 0.426840277 Age_cat
## 1168 3395 27 0 1 0.50142156 0.370617256 Age_cat
## 1169 3445 25 1 1 0.48136469 0.313163864 Age_cat
## 1170 3762 19 5 1 0.45602971 0.241820232 Age_cat
## 1171 3839 17 1 1 0.42920443 0.167446414 Age_cat
## 1172 3853 16 0 1 0.40237916 0.093914647 Age_cat
## 1173 4079 11 4 1 0.36579923 -0.005654623 Age_cat
## 1174 4191 9 1 1 0.32515487 -0.116407580 Age_cat
## 1175 41 49 0 1 0.97959184 3.881528369 Age_cat
## 1176 51 48 0 1 0.95918367 3.177909127 Age_cat
## 1177 140 47 0 1 0.93877551 2.761784869 Age_cat
## 1178 179 46 0 1 0.91836735 2.463249175 Age_cat
## 1179 191 45 0 1 0.89795918 2.229049689 Age_cat
## 1180 223 44 0 1 0.87755102 2.035461538 Age_cat
## 1181 334 43 0 1 0.85714286 1.869824714 Age_cat
## 1182 348 42 0 1 0.83673469 1.724578142 Age_cat
## 1183 388 41 0 1 0.81632653 1.594840751 Age_cat
## 1184 611 40 0 1 0.79591837 1.477275855 Age_cat
## 1185 762 39 0 1 0.77551020 1.369499632 Age_cat
## 1186 799 38 0 1 0.75510204 1.269748053 Age_cat
## 1187 850 37 0 1 0.73469388 1.176677534 Age_cat
## 1188 890 36 0 1 0.71428571 1.089239640 Age_cat
## 1189 904 35 0 1 0.69387755 1.006599065 Age_cat
## 1190 930 34 0 1 0.67346939 0.928078089 Age_cat
## 1191 1012 32 1 1 0.65242347 0.850827400 Age_cat
## 1192 1152 30 1 1 0.63067602 0.774437529 Age_cat
## 1193 1235 28 1 1 0.60815188 0.698500221 Age_cat
## 1194 1360 25 2 1 0.58382580 0.619613069 Age_cat
## 1195 1487 23 1 1 0.55844207 0.540246903 Age_cat
## 1196 1576 22 0 1 0.53305834 0.463426262 Age_cat
## 1197 1682 20 1 1 0.50640542 0.385048408 Age_cat
## 1198 1786 17 2 1 0.47661687 0.299697544 Age_cat
## 1199 2090 15 1 1 0.44484241 0.210677586 Age_cat
## 1200 2540 11 3 1 0.40440219 0.099438783 Age_cat
## 1201 2796 10 0 1 0.36396197 -0.010648984 Age_cat
## 1202 3090 7 2 1 0.31196741 -0.152597961 Age_cat
## 1203 3574 3 3 1 0.20797827 -0.451280487 Age_cat
## 1204 3584 2 0 1 0.10398914 -0.816898528 Age_catwrite.csv(logtransf_results, "Loglogtransf.csv", row.names = FALSE)Observed vs Expected
Drug
#Plot Observed vs Expected (Drug)
drug_obs = data.frame(Drug = factor(c(1, 2), levels = c(1, 2), labels = c("D-penicillamine", "Placebo")))
kmfit_drug = survfit(y ~ Drug, data = df) #observed
cox_drug = coxph(y ~ Drug, data = df) #expected
plot(kmfit_drug,
main = "Observed vs Expected Survival Probability for Drug",
xlab = "Time (day)", ylab = "Survival Probability",
lty = 1, col = c("red", "blue"))
lines(survfit(cox_drug, newdata = drug_obs), col = c("red", "blue"), lty = 2)
legend("topright",cex=0.6, legend = c("D-penicillamine (Obs)", "D-penicillamine (Exp)",
"Placebo (Obs)", "Placebo (Exp)"),
col = c("red", "red", "blue", "blue"), lty = c(1, 2, 1, 2),
title = "Drug")
### Sex
# Plot Observed vs Expected (Sex)
sex_obs = data.frame(Sex = factor(c(1, 2), levels = c(1, 2), labels = c("F", "M")))
kmfit_sex = survfit(y ~ Sex, data = df) # observed
cox_sex = coxph(y ~ Sex, data = df) # expected
plot(kmfit_sex,
main = "Observed vs Expected Survival Probability for Sex",
xlab = "Time (day)", ylab = "Survival Probability",
lty = 1, col = c("red", "blue"))
lines(survfit(cox_sex, newdata = sex_obs), col = c("red", "blue"), lty = 2)
legend("topright", cex=0.6, legend = c("Female (Obs)", "Female (Exp)",
"Male (Obs)", "Male (Exp)"),
col = c("red", "red", "blue", "blue"), lty = c(1, 2, 1, 2),
title = "Sex")
### Ascites
# Plot Observed vs Expected (Ascites)
ascites_obs = data.frame(Ascites = factor(c(1, 2), levels = c(1, 2), labels = c("N", "Y")))
kmfit_ascites = survfit(y ~ Ascites, data = df) # observed
cox_ascites = coxph(y ~ Ascites, data = df) # expected
plot(kmfit_ascites,
main = "Observed vs Expected Survival Probability for Ascites",
xlab = "Time (day)", ylab = "Survival Probability",
lty = 1, col = c("blue", "red"))
lines(survfit(cox_ascites, newdata = ascites_obs), col = c("blue", "red"), lty = 2)
legend("topright", cex=0.6, legend = c("No Ascites (Obs)", "No Ascites (Exp)",
"Ascites (Obs)", "Ascites (Exp)"),
col = c("blue", "blue", "red", "red"), lty = c(1, 2, 1, 2),
title = "Ascites")
### Spiders
# Plot Observed vs Expected (Spiders)
spiders_obs = data.frame(Spiders = factor(c(1, 2), levels = c(1, 2), labels = c("N", "Y")))
kmfit_spiders = survfit(y ~ Spiders, data = df) # observed
cox_spiders = coxph(y ~ Spiders, data = df) # expected
plot(kmfit_spiders,
main = "Observed vs Expected Survival Probability for Spiders",
xlab = "Time (day)", ylab = "Survival Probability",
lty = 1, col = c("blue", "red"))
lines(survfit(cox_spiders, newdata = spiders_obs), col = c("blue", "red"), lty = 2)
legend("topright", cex=0.6, legend = c("No Spiders (Obs)", "No Spiders (Exp)",
"Spiders (Obs)", "Spiders (Exp)"),
col = c("blue", "blue", "red", "red"), lty = c(1, 2, 1, 2),
title = "Spiders")
### Bilirubin_cat
# Plot Observed vs Expected (Bilirubin_cat)
bilirubin_obs = data.frame(Bilirubin_cat = factor(c(1, 2), levels = c(1, 2), labels = c("Normal", "Increased")))
kmfit_bilirubin = survfit(y ~ Bilirubin_cat, data = df) # observed
cox_bilirubin = coxph(y ~ Bilirubin_cat, data = df) # expected
plot(kmfit_bilirubin,
main = "Observed vs Expected Survival Probability for Bilirubin Category",
xlab = "Time (day)", ylab = "Survival Probability",
lty = 1, col = c("blue", "red"))
lines(survfit(cox_bilirubin, newdata = bilirubin_obs), col = c("blue", "red"), lty = 2)
legend("topright", cex=0.5, legend = c("Normal (Obs)", "Normal (Exp)",
"Increased (Obs)", "Increased (Exp)"),
col = c("blue", "blue", "red", "red"), lty = c(1, 2, 1, 2),
title = "Bilirubin Category")
Cholesterol_cat
# Plot Observed vs Expected (Cholesterol_cat)
cholesterol_obs = data.frame(Cholesterol_cat = factor(c(1, 2, 3), levels = c(1, 2, 3), labels = c("Normal", "Borderline", "High")))
kmfit_cholesterol = survfit(y ~ Cholesterol_cat, data = df) # observed
cox_cholesterol = coxph(y ~ Cholesterol_cat, data = df) # expected
plot(kmfit_cholesterol,
main = "Observed vs Expected Survival Probability for Cholesterol Category",
xlab = "Time (day)", ylab = "Survival Probability",
lty = 1, col = c("blue", "orange", "red"))
lines(survfit(cox_cholesterol, newdata = cholesterol_obs), col = c("blue", "orange", "red"), lty = 2)
legend("topright", cex=0.6, legend = c("Normal (Obs)", "Normal (Exp)",
"Borderline (Obs)", "Borderline (Exp)",
"High (Obs)", "High (Exp)"),
col = c("blue", "blue", "orange", "orange", "red", "red"),
lty = c(1, 2, 1, 2, 1, 2), title = "Cholesterol Category")
Platelets_cat
# Plot Observed vs Expected (Platelets_cat)
platelets_obs = data.frame(Platelets_cat = factor(c(1, 2, 3), levels = c(1, 2, 3), labels = c("Low", "Normal", "High")))
kmfit_platelets = survfit(y ~ Platelets_cat, data = df) # observed
cox_platelets = coxph(y ~ Platelets_cat, data = df) # expected
plot(kmfit_platelets,
main = "Observed vs Expected Survival Probability for Platelets Category",
xlab = "Time (day)", ylab = "Survival Probability",
lty = 1, col = c("blue", "orange", "red"))
lines(survfit(cox_platelets, newdata = platelets_obs), col = c("blue", "orange", "red"), lty = 2)
legend("topright", cex=0.6, legend = c("Low (Obs)", "Low (Exp)",
"Normal (Obs)", "Normal (Exp)",
"High (Obs)", "High (Exp)"),
col = c("blue", "blue", "orange", "orange", "red", "red"),
lty = c(1, 2, 1, 2, 1, 2), title = "Platelets Category")
Prothrombin_cat
# Plot Observed vs Expected (Prothrombin_cat)
prothrombin_obs = data.frame(Prothrombin_cat = factor(c(1, 2), levels = c(1, 2), labels = c("Normal", "Prolonged")))
kmfit_prothrombin = survfit(y ~ Prothrombin_cat, data = df) # observed
cox_prothrombin = coxph(y ~ Prothrombin_cat, data = df) # expected
plot(kmfit_prothrombin,
main = "Observed vs Expected Survival Probability for Prothrombin Category",
xlab = "Time (day)", ylab = "Survival Probability",
lty = 1, col = c("blue", "red"))
lines(survfit(cox_prothrombin, newdata = prothrombin_obs), col = c("blue", "red"), lty = 2)
legend("topright", cex=0.6, legend = c("Normal (Obs)", "Normal (Exp)",
"Prolonged (Obs)", "Prolonged (Exp)"),
col = c("blue", "blue", "red", "red"), lty = c(1, 2, 1, 2),
title = "Prothrombin Category")
Alk_phos_cat
# Plot Observed vs Expected (Alk_Phos_cat)
alkphos_obs = data.frame(Alk_Phos_cat = factor(c(1, 2), levels = c(1, 2), labels = c("Normal", "High")))
kmfit_alkphos = survfit(y ~ Alk_Phos_cat, data = df) # observed
cox_alkphos = coxph(y ~ Alk_Phos_cat, data = df) # expected
plot(kmfit_alkphos,
main = "Observed vs Expected Survival Probability for Alk Phos Category",
xlab = "Time (day)", ylab = "Survival Probability",
lty = 1, col = c("blue", "red"))
lines(survfit(cox_alkphos, newdata = alkphos_obs), col = c("blue", "red"), lty = 2)
legend("topright", cex=0.6, legend = c("Normal (Obs)", "Normal (Exp)",
"High (Obs)", "High (Exp)"),
col = c("blue", "blue", "red", "red"), lty = c(1, 2, 1, 2),
title = "Alk Phos Category")
SGOT_cat
# Plot Observed vs Expected (SGOT_cat)
sgot_obs = data.frame(SGOT_cat = factor(c(1, 2), levels = c(1, 2), labels = c("Normal", "High")))
kmfit_sgot = survfit(y ~ SGOT_cat, data = df) # observed
cox_sgot = coxph(y ~ SGOT_cat, data = df) # expected
plot(kmfit_sgot,
main = "Observed vs Expected Survival Probability for SGOT Category",
xlab = "Time (day)", ylab = "Survival Probability",
lty = 1, col = c("red", "blue"))
lines(survfit(cox_sgot, newdata = sgot_obs), col = c("blue", "red"), lty = 2)
legend("topright", cex=0.6, legend = c("Normal (Obs)", "Normal (Exp)",
"High (Obs)", "High (Exp)"),
col = c("blue", "blue", "red", "red"), lty = c(1, 2, 1, 2),
title = "SGOT Category")
Age_cat
# Plot Observed vs Expected (Age_cat)
age_obs = data.frame(Age_cat = factor(c(1, 2, 3), levels = c(1, 2, 3), labels = c("Young", "Middle-aged", "Old")))
kmfit_age = survfit(y ~ Age_cat, data = df) # observed
cox_age = coxph(y ~ Age_cat, data = df) # expected
plot(kmfit_age,
main = "Observed vs Expected Survival Probability for Age Category",
xlab = "Time (day)", ylab = "Survival Probability",
lty = 1, col = c("green", "blue", "red"))
lines(survfit(cox_age, newdata = age_obs), col = c("green", "blue", "red"), lty = 2)
legend("topright", cex=0.6, legend = c("Young (Obs)", "Young (Exp)",
"Middle-aged (Obs)", "Middle-aged (Exp)",
"Old (Obs)", "Old (Exp)"),
col = c("green", "green", "blue", "blue", "red", "red"),
lty = c(1, 2, 1, 2, 1, 2), title = "Age Category")
Goodnes Of Fit Test
ph_test = cox.zph(cox_model)
print(ph_test)## chisq df p
## Drug 0.17894 1 0.67229
## Age 3.82416 1 0.05052
## Sex 0.01123 1 0.91562
## Ascites 1.67162 1 0.19604
## Spiders 0.00262 1 0.95915
## Bilirubin 6.12010 1 0.01337
## Cholesterol 14.82483 1 0.00012
## Alk_Phos 0.73064 1 0.39268
## SGOT 0.10948 1 0.74074
## Platelets 3.11580 1 0.07754
## Prothrombin 2.02232 1 0.15500
## Stage 4.97879 3 0.17336
## GLOBAL 26.20689 14 0.02436Stratified COX Model
Kombinasi (Age, Bilirubin, Cholesterol) -> Z_star
df$Z_star = with(df,
ifelse(Bilirubin_cat == "Normal" & Cholesterol_cat == "Normal" & Age_cat == "Young", 1,
ifelse(Bilirubin_cat == "Normal" & Cholesterol_cat == "Normal" & Age_cat == "Middle-aged", 2,
ifelse(Bilirubin_cat == "Normal" & Cholesterol_cat == "Normal" & Age_cat == "Old", 3,
ifelse(Bilirubin_cat == "Normal" & Cholesterol_cat == "Borderline" & Age_cat == "Young", 4,
ifelse(Bilirubin_cat == "Normal" & Cholesterol_cat == "Borderline" & Age_cat == "Middle-aged", 5,
ifelse(Bilirubin_cat == "Normal" & Cholesterol_cat == "Borderline" & Age_cat == "Old", 6,
ifelse(Bilirubin_cat == "Normal" & Cholesterol_cat == "High" & Age_cat == "Young", 7,
ifelse(Bilirubin_cat == "Normal" & Cholesterol_cat == "High" & Age_cat == "Middle-aged", 8,
ifelse(Bilirubin_cat == "Normal" & Cholesterol_cat == "High" & Age_cat == "Old", 9,
ifelse(Bilirubin_cat == "Increased" & Cholesterol_cat == "Normal" & Age_cat == "Young", 10,
ifelse(Bilirubin_cat == "Increased" & Cholesterol_cat == "Normal" & Age_cat == "Middle-aged", 11,
ifelse(Bilirubin_cat == "Increased" & Cholesterol_cat == "Normal" & Age_cat == "Old", 12,
ifelse(Bilirubin_cat == "Increased" & Cholesterol_cat == "Borderline" & Age_cat == "Young", 13,
ifelse(Bilirubin_cat == "Increased" & Cholesterol_cat == "Borderline" & Age_cat == "Middle-aged", 14,
ifelse(Bilirubin_cat == "Increased" & Cholesterol_cat == "Borderline" & Age_cat == "Old", 15,
ifelse(Bilirubin_cat == "Increased" & Cholesterol_cat == "High" & Age_cat == "Young", 16,
ifelse(Bilirubin_cat == "Increased" & Cholesterol_cat == "High" & Age_cat == "Middle-aged", 17,
ifelse(Bilirubin_cat == "Increased" & Cholesterol_cat == "High" & Age_cat == "Old", 18, NA)))))))))))))))))))
head(df)## # A tibble: 6 × 23
## N_Days Status Drug Age Sex Ascites Spiders Bilirubin Cholesterol Alk_Phos
## <dbl> <fct> <fct> <dbl> <fct> <fct> <fct> <dbl> <dbl> <dbl>
## 1 400 D D-pe… 21464 F Y Y 14.5 261 1718
## 2 4500 C D-pe… 20617 F N Y 1.1 302 7395.
## 3 1012 D D-pe… 25594 M N N 1.4 176 516
## 4 1925 D D-pe… 19994 F N Y 1.8 244 6122.
## 5 1504 C Plac… 13918 F N Y 3.4 279 671
## 6 1832 C Plac… 20284 F N N 1 322 824
## # ℹ 13 more variables: SGOT <dbl>, Platelets <dbl>, Prothrombin <dbl>,
## # Stage <fct>, Age_years <dbl>, Bilirubin_cat <fct>, Cholesterol_cat <fct>,
## # Platelets_cat <fct>, Prothrombin_cat <fct>, Alk_Phos_cat <fct>,
## # SGOT_cat <fct>, Age_cat <fct>, Z_star <dbl>Model COX Stratified Tanpa Interaksi
strata_full = coxph(y ~ strata(Z_star) + Drug + Sex + Ascites + Spiders + Alk_Phos + SGOT + Platelets + Prothrombin + Stage,
data = df)
summary(strata_full)## Call:
## coxph(formula = y ~ strata(Z_star) + Drug + Sex + Ascites + Spiders +
## Alk_Phos + SGOT + Platelets + Prothrombin + Stage, data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DrugPlacebo -5.539e-02 9.461e-01 2.086e-01 -0.265 0.79066
## SexM 6.465e-02 1.067e+00 2.943e-01 0.220 0.82614
## AscitesY 9.092e-01 2.482e+00 3.607e-01 2.520 0.01172 *
## SpidersY 6.033e-01 1.828e+00 2.323e-01 2.597 0.00942 **
## Alk_Phos 6.524e-05 1.000e+00 3.950e-05 1.652 0.09856 .
## SGOT 5.242e-03 1.005e+00 1.690e-03 3.102 0.00192 **
## Platelets 8.224e-04 1.001e+00 1.170e-03 0.703 0.48192
## Prothrombin 2.798e-01 1.323e+00 1.249e-01 2.241 0.02505 *
## Stage2 5.506e-01 1.734e+00 1.075e+00 0.512 0.60854
## Stage3 9.797e-01 2.664e+00 1.045e+00 0.937 0.34863
## Stage4 1.244e+00 3.471e+00 1.059e+00 1.176 0.23974
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## DrugPlacebo 0.9461 1.0569 0.6286 1.424
## SexM 1.0668 0.9374 0.5992 1.899
## AscitesY 2.4822 0.4029 1.2241 5.034
## SpidersY 1.8281 0.5470 1.1594 2.882
## Alk_Phos 1.0001 0.9999 1.0000 1.000
## SGOT 1.0053 0.9948 1.0019 1.009
## Platelets 1.0008 0.9992 0.9985 1.003
## Prothrombin 1.3228 0.7560 1.0357 1.690
## Stage2 1.7343 0.5766 0.2109 14.262
## Stage3 2.6635 0.3754 0.3434 20.662
## Stage4 3.4711 0.2881 0.4359 27.638
##
## Concordance= 0.718 (se = 0.032 )
## Likelihood ratio test= 45.15 on 11 df, p=5e-06
## Wald test = 45.11 on 11 df, p=5e-06
## Score (logrank) test = 49.04 on 11 df, p=9e-07Model Strata 1 : Eliminasi variabel sex
strata1 = coxph(y ~ strata(Z_star) + Drug + Ascites + Spiders + Alk_Phos + SGOT + Platelets + Prothrombin + Stage,
data = df)
summary(strata1) ## Call:
## coxph(formula = y ~ strata(Z_star) + Drug + Ascites + Spiders +
## Alk_Phos + SGOT + Platelets + Prothrombin + Stage, data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DrugPlacebo -5.600e-02 9.455e-01 2.087e-01 -0.268 0.78840
## AscitesY 9.074e-01 2.478e+00 3.611e-01 2.513 0.01197 *
## SpidersY 5.948e-01 1.813e+00 2.289e-01 2.599 0.00935 **
## Alk_Phos 6.628e-05 1.000e+00 3.924e-05 1.689 0.09122 .
## SGOT 5.243e-03 1.005e+00 1.687e-03 3.108 0.00188 **
## Platelets 7.536e-04 1.001e+00 1.126e-03 0.669 0.50335
## Prothrombin 2.784e-01 1.321e+00 1.244e-01 2.238 0.02523 *
## Stage2 5.179e-01 1.679e+00 1.064e+00 0.487 0.62656
## Stage3 9.528e-01 2.593e+00 1.038e+00 0.918 0.35865
## Stage4 1.214e+00 3.366e+00 1.049e+00 1.157 0.24719
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## DrugPlacebo 0.9455 1.0576 0.6282 1.423
## AscitesY 2.4778 0.4036 1.2210 5.028
## SpidersY 1.8126 0.5517 1.1575 2.839
## Alk_Phos 1.0001 0.9999 1.0000 1.000
## SGOT 1.0053 0.9948 1.0019 1.009
## Platelets 1.0008 0.9992 0.9985 1.003
## Prothrombin 1.3211 0.7570 1.0352 1.686
## Stage2 1.6785 0.5958 0.2084 13.520
## Stage3 2.5929 0.3857 0.3391 19.829
## Stage4 3.3658 0.2971 0.4309 26.292
##
## Concordance= 0.718 (se = 0.032 )
## Likelihood ratio test= 45.1 on 10 df, p=2e-06
## Wald test = 44.95 on 10 df, p=2e-06
## Score (logrank) test = 48.78 on 10 df, p=4e-07Model Strata 2 : Eliminasi variabel sex dan drug
strata2 = coxph(y ~ strata(Z_star) + Ascites + Spiders + Alk_Phos + SGOT + Platelets + Prothrombin + Stage,
data = df)
summary(strata2) ## Call:
## coxph(formula = y ~ strata(Z_star) + Ascites + Spiders + Alk_Phos +
## SGOT + Platelets + Prothrombin + Stage, data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## AscitesY 9.115e-01 2.488e+00 3.617e-01 2.520 0.01173 *
## SpidersY 6.007e-01 1.823e+00 2.280e-01 2.634 0.00843 **
## Alk_Phos 6.677e-05 1.000e+00 3.919e-05 1.704 0.08842 .
## SGOT 5.215e-03 1.005e+00 1.677e-03 3.109 0.00188 **
## Platelets 7.432e-04 1.001e+00 1.129e-03 0.658 0.51032
## Prothrombin 2.749e-01 1.316e+00 1.237e-01 2.221 0.02632 *
## Stage2 4.963e-01 1.643e+00 1.062e+00 0.467 0.64022
## Stage3 9.252e-01 2.522e+00 1.033e+00 0.895 0.37054
## Stage4 1.181e+00 3.258e+00 1.042e+00 1.133 0.25701
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## AscitesY 2.488 0.4019 1.2246 5.055
## SpidersY 1.823 0.5484 1.1662 2.851
## Alk_Phos 1.000 0.9999 1.0000 1.000
## SGOT 1.005 0.9948 1.0019 1.009
## Platelets 1.001 0.9993 0.9985 1.003
## Prothrombin 1.316 0.7597 1.0329 1.678
## Stage2 1.643 0.6088 0.2050 13.163
## Stage3 2.522 0.3965 0.3329 19.109
## Stage4 3.258 0.3070 0.4227 25.111
##
## Concordance= 0.718 (se = 0.032 )
## Likelihood ratio test= 45.03 on 9 df, p=9e-07
## Wald test = 44.81 on 9 df, p=1e-06
## Score (logrank) test = 48.69 on 9 df, p=2e-07Model Strata 3 : Eliminasi variabel sex, drug, dan stage
strata3 = coxph(y ~ strata(Z_star) + Ascites + Spiders + Alk_Phos + SGOT + Platelets + Prothrombin,
data = df)
summary(strata3) ## Call:
## coxph(formula = y ~ strata(Z_star) + Ascites + Spiders + Alk_Phos +
## SGOT + Platelets + Prothrombin, data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## AscitesY 1.027e+00 2.792e+00 3.410e-01 3.011 0.00261 **
## SpidersY 7.036e-01 2.021e+00 2.238e-01 3.144 0.00167 **
## Alk_Phos 5.646e-05 1.000e+00 3.725e-05 1.516 0.12964
## SGOT 4.466e-03 1.004e+00 1.616e-03 2.763 0.00573 **
## Platelets 5.405e-04 1.001e+00 1.113e-03 0.486 0.62721
## Prothrombin 3.277e-01 1.388e+00 1.207e-01 2.716 0.00660 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## AscitesY 2.792 0.3582 1.4310 5.448
## SpidersY 2.021 0.4948 1.3033 3.134
## Alk_Phos 1.000 0.9999 1.0000 1.000
## SGOT 1.004 0.9955 1.0013 1.008
## Platelets 1.001 0.9995 0.9984 1.003
## Prothrombin 1.388 0.7205 1.0955 1.758
##
## Concordance= 0.705 (se = 0.034 )
## Likelihood ratio test= 40.72 on 6 df, p=3e-07
## Wald test = 41.74 on 6 df, p=2e-07
## Score (logrank) test = 45.21 on 6 df, p=4e-08Model Strata 4 : Eliminasi variabel sex, drug, stage dan platelets
strata4 = coxph(y ~ strata(Z_star) + Ascites + Spiders + Alk_Phos + SGOT + Prothrombin,
data = df)
summary(strata4) ## Call:
## coxph(formula = y ~ strata(Z_star) + Ascites + Spiders + Alk_Phos +
## SGOT + Prothrombin, data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## AscitesY 1.003e+00 2.727e+00 3.370e-01 2.976 0.00292 **
## SpidersY 6.871e-01 1.988e+00 2.213e-01 3.105 0.00190 **
## Alk_Phos 5.921e-05 1.000e+00 3.675e-05 1.611 0.10717
## SGOT 4.399e-03 1.004e+00 1.611e-03 2.732 0.00630 **
## Prothrombin 3.321e-01 1.394e+00 1.205e-01 2.755 0.00587 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## AscitesY 2.727 0.3667 1.409 5.279
## SpidersY 1.988 0.5030 1.288 3.067
## Alk_Phos 1.000 0.9999 1.000 1.000
## SGOT 1.004 0.9956 1.001 1.008
## Prothrombin 1.394 0.7174 1.101 1.765
##
## Concordance= 0.705 (se = 0.034 )
## Likelihood ratio test= 40.48 on 5 df, p=1e-07
## Wald test = 41.44 on 5 df, p=8e-08
## Score (logrank) test = 44.95 on 5 df, p=1e-08Model Strata 5 : Eliminasi variabel sex, drug, stage, platelets dan alk_phos
strata5 = coxph(y ~ strata(Z_star) + Ascites + Spiders + SGOT + Prothrombin,
data = df)
summary(strata5) ## Call:
## coxph(formula = y ~ strata(Z_star) + Ascites + Spiders + SGOT +
## Prothrombin, data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## AscitesY 0.999340 2.716488 0.337850 2.958 0.00310 **
## SpidersY 0.630170 1.877929 0.217977 2.891 0.00384 **
## SGOT 0.004500 1.004510 0.001589 2.831 0.00464 **
## Prothrombin 0.349094 1.417782 0.120066 2.908 0.00364 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## AscitesY 2.716 0.3681 1.401 5.267
## SpidersY 1.878 0.5325 1.225 2.879
## SGOT 1.005 0.9955 1.001 1.008
## Prothrombin 1.418 0.7053 1.120 1.794
##
## Concordance= 0.704 (se = 0.034 )
## Likelihood ratio test= 38.08 on 4 df, p=1e-07
## Wald test = 39.55 on 4 df, p=5e-08
## Score (logrank) test = 42.87 on 4 df, p=1e-08Perbandingan : Mana model strata tanpa interaksi yang terbaik?
model_list = list(
strata_full = strata_full,
strata1 = strata1,
strata2 = strata2,
strata3 = strata3,
strata4 = strata4,
strata5 = strata5
)
stra_summary = data.frame()
for (model_name in names(model_list)) {
model = model_list[[model_name]]
model_summary_data = summary(model)
variables = rownames(model_summary_data$coefficients)
HR = model_summary_data$coefficients[, "exp(coef)"]
CI_lower = model_summary_data$conf.int[, "lower .95"]
CI_upper = model_summary_data$conf.int[, "upper .95"]
model_AIC = AIC(model)
temp_df = data.frame(
Model = model_name,
Variable = variables,
HR = HR,
CI_Lower = CI_lower,
CI_Upper = CI_upper,
AIC = model_AIC
)
stra_summary = rbind(stra_summary, temp_df)
}
print(stra_summary)## Model Variable HR CI_Lower CI_Upper AIC
## DrugPlacebo strata_full DrugPlacebo 0.9461190 0.6285562 1.424123 596.1547
## SexM strata_full SexM 1.0667837 0.5991643 1.899358 596.1547
## AscitesY strata_full AscitesY 2.4822367 1.2240740 5.033600 596.1547
## SpidersY strata_full SpidersY 1.8280860 1.1594092 2.882415 596.1547
## Alk_Phos strata_full Alk_Phos 1.0000652 0.9999878 1.000143 596.1547
## SGOT strata_full SGOT 1.0052554 1.0019313 1.008591 596.1547
## Platelets strata_full Platelets 1.0008228 0.9985312 1.003120 596.1547
## Prothrombin strata_full Prothrombin 1.3228301 1.0356674 1.689615 596.1547
## Stage2 strata_full Stage2 1.7342739 0.2108856 14.262264 596.1547
## Stage3 strata_full Stage3 2.6635463 0.3433570 20.662108 596.1547
## Stage4 strata_full Stage4 3.4711017 0.4359488 27.637530 596.1547
## DrugPlacebo1 strata1 DrugPlacebo 0.9455376 0.6281621 1.423265 594.2026
## AscitesY1 strata1 AscitesY 2.4777718 1.2209939 5.028161 594.2026
## SpidersY1 strata1 SpidersY 1.8126408 1.1574517 2.838707 594.2026
## Alk_Phos1 strata1 Alk_Phos 1.0000663 0.9999894 1.000143 594.2026
## SGOT1 strata1 SGOT 1.0052563 1.0019383 1.008585 594.2026
## Platelets1 strata1 Platelets 1.0007539 0.9985476 1.002965 594.2026
## Prothrombin1 strata1 Prothrombin 1.3210621 1.0351874 1.685883 594.2026
## Stage21 strata1 Stage2 1.6785487 0.2083933 13.520229 594.2026
## Stage31 strata1 Stage3 2.5929322 0.3390609 19.829175 594.2026
## Stage41 strata1 Stage4 3.3657947 0.4308816 26.291616 594.2026
## AscitesY2 strata2 AscitesY 2.4880955 1.2246105 5.055174 592.2746
## SpidersY2 strata2 SpidersY 1.8233423 1.1662176 2.850735 592.2746
## Alk_Phos2 strata2 Alk_Phos 1.0000668 0.9999900 1.000144 592.2746
## SGOT2 strata2 SGOT 1.0052288 1.0019292 1.008539 592.2746
## Platelets2 strata2 Platelets 1.0007435 0.9985317 1.002960 592.2746
## Prothrombin2 strata2 Prothrombin 1.3163781 1.0328854 1.677680 592.2746
## Stage22 strata2 Stage2 1.6426113 0.2049821 13.162964 592.2746
## Stage32 strata2 Stage3 2.5223103 0.3329273 19.109424 592.2746
## Stage42 strata2 Stage4 3.2578429 0.4226670 25.110882 592.2746
## AscitesY3 strata3 AscitesY 2.7920549 1.4310009 5.447635 590.5857
## SpidersY3 strata3 SpidersY 2.0210522 1.3033307 3.134010 590.5857
## Alk_Phos3 strata3 Alk_Phos 1.0000565 0.9999834 1.000129 590.5857
## SGOT3 strata3 SGOT 1.0044757 1.0012983 1.007663 590.5857
## Platelets3 strata3 Platelets 1.0005406 0.9983606 1.002725 590.5857
## Prothrombin3 strata3 Prothrombin 1.3878362 1.0955488 1.758104 590.5857
## AscitesY4 strata4 AscitesY 2.7268455 1.4085463 5.278979 588.8202
## SpidersY4 strata4 SpidersY 1.9879294 1.2884591 3.067124 588.8202
## Alk_Phos4 strata4 Alk_Phos 1.0000592 0.9999872 1.000131 588.8202
## SGOT4 strata4 SGOT 1.0044089 1.0012434 1.007584 588.8202
## Prothrombin4 strata4 Prothrombin 1.3939201 1.1005909 1.765427 588.8202
## AscitesY5 strata5 AscitesY 2.7164885 1.4009722 5.267278 589.2272
## SpidersY5 strata5 SpidersY 1.8779292 1.2250024 2.878866 589.2272
## SGOT5 strata5 SGOT 1.0045100 1.0013858 1.007644 589.2272
## Prothrombin5 strata5 Prothrombin 1.4177822 1.1204934 1.793948 589.2272write.csv(stra_summary, "stratifiedmodel.csv", row.names = FALSE)Model Strata Final : Model Strata 5
strata_model_final = strata5
summary(strata_model_final)## Call:
## coxph(formula = y ~ strata(Z_star) + Ascites + Spiders + SGOT +
## Prothrombin, data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## AscitesY 0.999340 2.716488 0.337850 2.958 0.00310 **
## SpidersY 0.630170 1.877929 0.217977 2.891 0.00384 **
## SGOT 0.004500 1.004510 0.001589 2.831 0.00464 **
## Prothrombin 0.349094 1.417782 0.120066 2.908 0.00364 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## AscitesY 2.716 0.3681 1.401 5.267
## SpidersY 1.878 0.5325 1.225 2.879
## SGOT 1.005 0.9955 1.001 1.008
## Prothrombin 1.418 0.7053 1.120 1.794
##
## Concordance= 0.704 (se = 0.034 )
## Likelihood ratio test= 38.08 on 4 df, p=1e-07
## Wald test = 39.55 on 4 df, p=5e-08
## Score (logrank) test = 42.87 on 4 df, p=1e-08stra_base_haz = basehaz(strata_model_final, centered = FALSE)
print(stra_base_haz)## hazard time strata
## 1 0.000000e+00 1735 Z_star=1
## 2 0.000000e+00 2221 Z_star=1
## 3 0.000000e+00 4039 Z_star=1
## 4 0.000000e+00 4190 Z_star=1
## 5 0.000000e+00 1433 Z_star=2
## 6 0.000000e+00 2504 Z_star=2
## 7 9.869487e-03 2583 Z_star=2
## 8 9.869487e-03 3611 Z_star=2
## 9 0.000000e+00 2332 Z_star=3
## 10 0.000000e+00 1230 Z_star=4
## 11 0.000000e+00 1525 Z_star=4
## 12 0.000000e+00 1967 Z_star=4
## 13 0.000000e+00 1457 Z_star=5
## 14 0.000000e+00 1481 Z_star=5
## 15 0.000000e+00 1831 Z_star=5
## 16 0.000000e+00 1978 Z_star=5
## 17 1.123837e-03 2055 Z_star=5
## 18 1.123837e-03 2241 Z_star=5
## 19 1.123837e-03 2294 Z_star=5
## 20 1.123837e-03 2666 Z_star=5
## 21 1.123837e-03 2772 Z_star=5
## 22 1.123837e-03 2863 Z_star=5
## 23 3.449110e-03 3170 Z_star=5
## 24 3.449110e-03 3672 Z_star=5
## 25 3.449110e-03 4232 Z_star=5
## 26 3.449110e-03 4256 Z_star=5
## 27 0.000000e+00 2255 Z_star=6
## 28 0.000000e+00 2272 Z_star=6
## 29 0.000000e+00 3388 Z_star=6
## 30 1.209801e-02 3584 Z_star=6
## 31 7.529782e-04 198 Z_star=7
## 32 7.529782e-04 1149 Z_star=7
## 33 7.529782e-04 1271 Z_star=7
## 34 7.529782e-04 1301 Z_star=7
## 35 7.529782e-04 1321 Z_star=7
## 36 7.529782e-04 1434 Z_star=7
## 37 7.529782e-04 1568 Z_star=7
## 38 7.529782e-04 1701 Z_star=7
## 39 7.529782e-04 1776 Z_star=7
## 40 2.704706e-03 1847 Z_star=7
## 41 2.704706e-03 1945 Z_star=7
## 42 2.704706e-03 2022 Z_star=7
## 43 2.704706e-03 2357 Z_star=7
## 44 2.704706e-03 3092 Z_star=7
## 45 2.704706e-03 3823 Z_star=7
## 46 2.704706e-03 4184 Z_star=7
## 47 2.414031e-04 515 Z_star=8
## 48 4.875793e-04 694 Z_star=8
## 49 4.875793e-04 994 Z_star=8
## 50 4.875793e-04 1216 Z_star=8
## 51 4.875793e-04 1295 Z_star=8
## 52 4.875793e-04 1300 Z_star=8
## 53 4.875793e-04 1401 Z_star=8
## 54 4.875793e-04 1569 Z_star=8
## 55 4.875793e-04 1614 Z_star=8
## 56 4.875793e-04 1702 Z_star=8
## 57 4.875793e-04 1769 Z_star=8
## 58 4.875793e-04 1785 Z_star=8
## 59 4.875793e-04 1790 Z_star=8
## 60 4.875793e-04 1832 Z_star=8
## 61 4.875793e-04 1932 Z_star=8
## 62 4.875793e-04 1951 Z_star=8
## 63 4.875793e-04 2050 Z_star=8
## 64 4.875793e-04 2106 Z_star=8
## 65 4.875793e-04 2178 Z_star=8
## 66 4.875793e-04 2216 Z_star=8
## 67 8.548509e-04 2224 Z_star=8
## 68 1.249854e-03 2297 Z_star=8
## 69 1.249854e-03 2365 Z_star=8
## 70 1.662774e-03 2419 Z_star=8
## 71 1.662774e-03 2443 Z_star=8
## 72 1.662774e-03 2452 Z_star=8
## 73 2.111144e-03 2466 Z_star=8
## 74 2.111144e-03 2527 Z_star=8
## 75 2.111144e-03 2576 Z_star=8
## 76 2.598555e-03 2598 Z_star=8
## 77 2.598555e-03 2609 Z_star=8
## 78 2.598555e-03 2624 Z_star=8
## 79 3.129077e-03 2769 Z_star=8
## 80 3.129077e-03 2835 Z_star=8
## 81 3.129077e-03 2870 Z_star=8
## 82 3.129077e-03 2976 Z_star=8
## 83 3.129077e-03 3050 Z_star=8
## 84 3.129077e-03 3059 Z_star=8
## 85 3.129077e-03 3069 Z_star=8
## 86 3.880410e-03 3086 Z_star=8
## 87 3.880410e-03 3098 Z_star=8
## 88 3.880410e-03 3099 Z_star=8
## 89 3.880410e-03 3149 Z_star=8
## 90 3.880410e-03 3150 Z_star=8
## 91 3.880410e-03 3255 Z_star=8
## 92 3.880410e-03 3297 Z_star=8
## 93 3.880410e-03 3422 Z_star=8
## 94 3.880410e-03 3577 Z_star=8
## 95 3.880410e-03 3581 Z_star=8
## 96 3.880410e-03 3707 Z_star=8
## 97 5.680809e-03 3853 Z_star=8
## 98 5.680809e-03 3933 Z_star=8
## 99 5.680809e-03 4032 Z_star=8
## 100 5.680809e-03 4127 Z_star=8
## 101 5.680809e-03 4365 Z_star=8
## 102 5.680809e-03 4500 Z_star=8
## 103 5.680809e-03 4556 Z_star=8
## 104 0.000000e+00 1030 Z_star=9
## 105 0.000000e+00 1153 Z_star=9
## 106 0.000000e+00 1250 Z_star=9
## 107 1.960749e-03 1682 Z_star=9
## 108 1.960749e-03 1770 Z_star=9
## 109 4.782734e-03 1786 Z_star=9
## 110 8.017608e-03 2090 Z_star=9
## 111 8.017608e-03 2990 Z_star=9
## 112 8.017608e-03 3445 Z_star=9
## 113 8.017608e-03 4509 Z_star=9
## 114 5.040556e-04 77 Z_star=11
## 115 1.192950e-03 110 Z_star=11
## 116 2.162815e-03 131 Z_star=11
## 117 4.057924e-03 552 Z_star=11
## 118 6.685656e-03 1077 Z_star=11
## 119 6.685656e-03 1320 Z_star=11
## 120 4.302500e-04 41 Z_star=12
## 121 1.262619e-03 140 Z_star=12
## 122 2.583413e-03 191 Z_star=12
## 123 4.957108e-03 348 Z_star=12
## 124 1.479448e-02 1012 Z_star=12
## 125 0.000000e+00 737 Z_star=13
## 126 0.000000e+00 2318 Z_star=13
## 127 0.000000e+00 2657 Z_star=13
## 128 4.984691e-04 304 Z_star=14
## 129 1.089230e-03 549 Z_star=14
## 130 2.827021e-03 750 Z_star=14
## 131 2.827021e-03 2157 Z_star=14
## 132 2.827021e-03 2176 Z_star=14
## 133 2.827021e-03 2301 Z_star=14
## 134 2.827021e-03 2363 Z_star=14
## 135 5.952448e-04 51 Z_star=15
## 136 1.478833e-03 179 Z_star=15
## 137 3.232320e-03 388 Z_star=15
## 138 6.724913e-03 1576 Z_star=15
## 139 6.724913e-03 1656 Z_star=15
## 140 3.330188e-04 733 Z_star=16
## 141 3.330188e-04 788 Z_star=16
## 142 6.977074e-04 790 Z_star=16
## 143 6.977074e-04 839 Z_star=16
## 144 6.977074e-04 877 Z_star=16
## 145 1.093679e-03 974 Z_star=16
## 146 1.500112e-03 1212 Z_star=16
## 147 1.500112e-03 1293 Z_star=16
## 148 1.500112e-03 1349 Z_star=16
## 149 1.500112e-03 1408 Z_star=16
## 150 1.500112e-03 1420 Z_star=16
## 151 1.977076e-03 1427 Z_star=16
## 152 2.471670e-03 1434 Z_star=16
## 153 2.471670e-03 1435 Z_star=16
## 154 2.471670e-03 1455 Z_star=16
## 155 2.471670e-03 1504 Z_star=16
## 156 2.471670e-03 1882 Z_star=16
## 157 2.471670e-03 1908 Z_star=16
## 158 2.471670e-03 1979 Z_star=16
## 159 2.471670e-03 2033 Z_star=16
## 160 3.519207e-03 2105 Z_star=16
## 161 3.519207e-03 2330 Z_star=16
## 162 3.519207e-03 2475 Z_star=16
## 163 5.235208e-03 2689 Z_star=16
## 164 7.273585e-03 3244 Z_star=16
## 165 1.032199e-02 3428 Z_star=16
## 166 1.032199e-02 3913 Z_star=16
## 167 7.272501e-05 71 Z_star=17
## 168 1.466129e-04 186 Z_star=17
## 169 2.217187e-04 216 Z_star=17
## 170 3.100606e-04 264 Z_star=17
## 171 4.055001e-04 321 Z_star=17
## 172 5.023332e-04 326 Z_star=17
## 173 6.018281e-04 400 Z_star=17
## 174 7.084440e-04 460 Z_star=17
## 175 7.084440e-04 533 Z_star=17
## 176 8.202454e-04 597 Z_star=17
## 177 9.340975e-04 673 Z_star=17
## 178 9.340975e-04 732 Z_star=17
## 179 1.051319e-03 769 Z_star=17
## 180 1.169460e-03 786 Z_star=17
## 181 1.288804e-03 797 Z_star=17
## 182 1.288804e-03 837 Z_star=17
## 183 1.410683e-03 853 Z_star=17
## 184 1.534634e-03 859 Z_star=17
## 185 1.534634e-03 901 Z_star=17
## 186 1.665120e-03 943 Z_star=17
## 187 1.796984e-03 980 Z_star=17
## 188 1.931717e-03 999 Z_star=17
## 189 1.931717e-03 1067 Z_star=17
## 190 2.068564e-03 1080 Z_star=17
## 191 2.207396e-03 1083 Z_star=17
## 192 2.207396e-03 1084 Z_star=17
## 193 2.351107e-03 1165 Z_star=17
## 194 2.498987e-03 1170 Z_star=17
## 195 2.822970e-03 1191 Z_star=17
## 196 2.822970e-03 1216 Z_star=17
## 197 2.822970e-03 1234 Z_star=17
## 198 3.016280e-03 1297 Z_star=17
## 199 3.016280e-03 1329 Z_star=17
## 200 3.215905e-03 1356 Z_star=17
## 201 3.215905e-03 1363 Z_star=17
## 202 3.215905e-03 1412 Z_star=17
## 203 3.428044e-03 1413 Z_star=17
## 204 3.428044e-03 1418 Z_star=17
## 205 3.652559e-03 1444 Z_star=17
## 206 3.883584e-03 1536 Z_star=17
## 207 3.883584e-03 1542 Z_star=17
## 208 3.883584e-03 1558 Z_star=17
## 209 3.883584e-03 1592 Z_star=17
## 210 3.883584e-03 1615 Z_star=17
## 211 4.139206e-03 1657 Z_star=17
## 212 4.139206e-03 1666 Z_star=17
## 213 4.682734e-03 1690 Z_star=17
## 214 4.970227e-03 1741 Z_star=17
## 215 4.970227e-03 1783 Z_star=17
## 216 5.280454e-03 1827 Z_star=17
## 217 5.280454e-03 1882 Z_star=17
## 218 5.609118e-03 1925 Z_star=17
## 219 5.947787e-03 2256 Z_star=17
## 220 6.305326e-03 2288 Z_star=17
## 221 6.305326e-03 2350 Z_star=17
## 222 6.684035e-03 2386 Z_star=17
## 223 7.078027e-03 2400 Z_star=17
## 224 7.078027e-03 2449 Z_star=17
## 225 7.078027e-03 2456 Z_star=17
## 226 7.078027e-03 2468 Z_star=17
## 227 7.078027e-03 2556 Z_star=17
## 228 7.078027e-03 2563 Z_star=17
## 229 7.078027e-03 2573 Z_star=17
## 230 7.078027e-03 2692 Z_star=17
## 231 7.078027e-03 2721 Z_star=17
## 232 7.078027e-03 2797 Z_star=17
## 233 7.680290e-03 2847 Z_star=17
## 234 7.680290e-03 2944 Z_star=17
## 235 8.325377e-03 3282 Z_star=17
## 236 9.025615e-03 3358 Z_star=17
## 237 9.769074e-03 3395 Z_star=17
## 238 1.057062e-02 3445 Z_star=17
## 239 1.160427e-02 3762 Z_star=17
## 240 1.160427e-02 3820 Z_star=17
## 241 1.296795e-02 3839 Z_star=17
## 242 1.296795e-02 3992 Z_star=17
## 243 1.296795e-02 4050 Z_star=17
## 244 1.516731e-02 4079 Z_star=17
## 245 1.787979e-02 4191 Z_star=17
## 246 1.787979e-02 4196 Z_star=17
## 247 1.787979e-02 4427 Z_star=17
## 248 1.787979e-02 4523 Z_star=17
## 249 2.874178e-04 223 Z_star=18
## 250 6.125826e-04 334 Z_star=18
## 251 9.641534e-04 611 Z_star=18
## 252 1.325353e-03 762 Z_star=18
## 253 1.699290e-03 799 Z_star=18
## 254 2.079636e-03 850 Z_star=18
## 255 2.517991e-03 890 Z_star=18
## 256 2.978660e-03 904 Z_star=18
## 257 3.453652e-03 930 Z_star=18
## 258 3.453652e-03 939 Z_star=18
## 259 3.966683e-03 1152 Z_star=18
## 260 4.500324e-03 1235 Z_star=18
## 261 4.500324e-03 1302 Z_star=18
## 262 5.137011e-03 1360 Z_star=18
## 263 5.137011e-03 1363 Z_star=18
## 264 5.823144e-03 1487 Z_star=18
## 265 5.823144e-03 1765 Z_star=18
## 266 5.823144e-03 1810 Z_star=18
## 267 7.120756e-03 2540 Z_star=18
## 268 8.722778e-03 2796 Z_star=18
## 269 8.722778e-03 2995 Z_star=18
## 270 1.068608e-02 3090 Z_star=18
## 271 1.068608e-02 3336 Z_star=18
## 272 2.290183e-02 3574 Z_star=18Asumsi PH Model Strata Final
ph.test_stra = cox.zph(strata_model_final)
print(ph.test_stra)## chisq df p
## Ascites 2.88512 1 0.089
## Spiders 0.00101 1 0.975
## SGOT 0.17190 1 0.678
## Prothrombin 2.47512 1 0.116
## GLOBAL 4.99145 4 0.288Model COX Stratified dengan Interaksi
strata.int_full = coxph(y ~ Drug + Age + Sex + Ascites + Spiders + Alk_Phos + SGOT + Platelets + Prothrombin + Stage +
Drug*Z_star + Age*Z_star + Sex*Z_star + Ascites*Z_star + Spiders*Z_star + Alk_Phos*Z_star + SGOT*Z_star + Platelets*Z_star + Prothrombin*Z_star + Stage*Z_star +
strata(Z_star),
data = df)## Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
## Loglik converged before variable 10,11,12,23,24,25 ; coefficient may be
## infinite.summary(strata.int_full)## Call:
## coxph(formula = y ~ Drug + Age + Sex + Ascites + Spiders + Alk_Phos +
## SGOT + Platelets + Prothrombin + Stage + Drug * Z_star +
## Age * Z_star + Sex * Z_star + Ascites * Z_star + Spiders *
## Z_star + Alk_Phos * Z_star + SGOT * Z_star + Platelets *
## Z_star + Prothrombin * Z_star + Stage * Z_star + strata(Z_star),
## data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## DrugPlacebo -1.213e+00 2.972e-01 9.340e-01 -1.299 0.19393
## Age 1.675e-04 1.000e+00 3.061e-04 0.547 0.58424
## SexM 7.490e-01 2.115e+00 1.954e+00 0.383 0.70146
## AscitesY 9.355e+00 1.155e+04 3.629e+00 2.578 0.00994 **
## SpidersY -5.537e-02 9.461e-01 1.272e+00 -0.044 0.96527
## Alk_Phos 2.690e-04 1.000e+00 1.842e-04 1.460 0.14421
## SGOT 8.564e-03 1.009e+00 7.963e-03 1.075 0.28217
## Platelets 8.024e-03 1.008e+00 6.047e-03 1.327 0.18456
## Prothrombin 2.136e-01 1.238e+00 8.839e-01 0.242 0.80905
## Stage2 2.629e+01 2.628e+11 6.759e+03 0.004 0.99690
## Stage3 2.762e+01 9.879e+11 6.759e+03 0.004 0.99674
## Stage4 2.762e+01 9.841e+11 6.759e+03 0.004 0.99674
## Z_star NA NA 0.000e+00 NA NA
## DrugPlacebo:Z_star 7.999e-02 1.083e+00 6.053e-02 1.322 0.18632
## Age:Z_star -3.852e-06 1.000e+00 1.892e-05 -0.204 0.83866
## SexM:Z_star -3.585e-02 9.648e-01 1.209e-01 -0.297 0.76676
## AscitesY:Z_star -5.133e-01 5.985e-01 2.176e-01 -2.359 0.01834 *
## SpidersY:Z_star 4.076e-02 1.042e+00 7.929e-02 0.514 0.60724
## Alk_Phos:Z_star -1.381e-05 1.000e+00 1.163e-05 -1.187 0.23539
## SGOT:Z_star -1.876e-04 9.998e-01 5.125e-04 -0.366 0.71429
## Platelets:Z_star -4.354e-04 9.996e-01 3.780e-04 -1.152 0.24940
## Prothrombin:Z_star 8.839e-03 1.009e+00 5.359e-02 0.165 0.86898
## Stage2:Z_star -1.541e+00 2.142e-01 3.976e+02 -0.004 0.99691
## Stage3:Z_star -1.604e+00 2.011e-01 3.976e+02 -0.004 0.99678
## Stage4:Z_star -1.598e+00 2.024e-01 3.976e+02 -0.004 0.99679
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## DrugPlacebo 2.972e-01 3.365e+00 0.04764 1.854e+00
## Age 1.000e+00 9.998e-01 0.99957 1.001e+00
## SexM 2.115e+00 4.728e-01 0.04593 9.739e+01
## AscitesY 1.155e+04 8.655e-05 9.41989 1.417e+07
## SpidersY 9.461e-01 1.057e+00 0.07824 1.144e+01
## Alk_Phos 1.000e+00 9.997e-01 0.99991 1.001e+00
## SGOT 1.009e+00 9.915e-01 0.99298 1.024e+00
## Platelets 1.008e+00 9.920e-01 0.99618 1.020e+00
## Prothrombin 1.238e+00 8.077e-01 0.21899 7.000e+00
## Stage2 2.628e+11 3.805e-12 0.00000 Inf
## Stage3 9.879e+11 1.012e-12 0.00000 Inf
## Stage4 9.841e+11 1.016e-12 0.00000 Inf
## Z_star NA NA NA NA
## DrugPlacebo:Z_star 1.083e+00 9.231e-01 0.96209 1.220e+00
## Age:Z_star 1.000e+00 1.000e+00 0.99996 1.000e+00
## SexM:Z_star 9.648e-01 1.037e+00 0.76128 1.223e+00
## AscitesY:Z_star 5.985e-01 1.671e+00 0.39068 9.169e-01
## SpidersY:Z_star 1.042e+00 9.601e-01 0.89168 1.217e+00
## Alk_Phos:Z_star 1.000e+00 1.000e+00 0.99996 1.000e+00
## SGOT:Z_star 9.998e-01 1.000e+00 0.99881 1.001e+00
## Platelets:Z_star 9.996e-01 1.000e+00 0.99882 1.000e+00
## Prothrombin:Z_star 1.009e+00 9.912e-01 0.90829 1.121e+00
## Stage2:Z_star 2.142e-01 4.669e+00 0.00000 Inf
## Stage3:Z_star 2.011e-01 4.972e+00 0.00000 Inf
## Stage4:Z_star 2.024e-01 4.941e+00 0.00000 Inf
##
## Concordance= 0.741 (se = 0.03 )
## Likelihood ratio test= 60.74 on 24 df, p=5e-05
## Wald test = 23.58 on 24 df, p=0.5
## Score (logrank) test = 66.24 on 24 df, p=8e-06Model Strata Int 1 : Tanpa drug dan interaksinya
strata.int1 = coxph(y ~ Age + Sex + Ascites + Spiders + Alk_Phos + SGOT + Platelets + Prothrombin + Stage
+ Age*Z_star + Sex*Z_star + Ascites*Z_star + Spiders*Z_star + Alk_Phos*Z_star + SGOT*Z_star + Platelets*Z_star + Prothrombin*Z_star + Stage*Z_star +
strata(Z_star),
data = df) ## Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
## Loglik converged before variable 9,10,11,21,22,23 ; coefficient may be infinite.summary(strata.int1)## Call:
## coxph(formula = y ~ Age + Sex + Ascites + Spiders + Alk_Phos +
## SGOT + Platelets + Prothrombin + Stage + Age * Z_star + Sex *
## Z_star + Ascites * Z_star + Spiders * Z_star + Alk_Phos *
## Z_star + SGOT * Z_star + Platelets * Z_star + Prothrombin *
## Z_star + Stage * Z_star + strata(Z_star), data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Age 2.064e-04 1.000e+00 2.973e-04 0.694 0.4876
## SexM 6.946e-01 2.003e+00 1.931e+00 0.360 0.7191
## AscitesY 8.844e+00 6.934e+03 3.601e+00 2.456 0.0141 *
## SpidersY -4.885e-02 9.523e-01 1.242e+00 -0.039 0.9686
## Alk_Phos 2.870e-04 1.000e+00 1.802e-04 1.593 0.1112
## SGOT 7.346e-03 1.007e+00 7.491e-03 0.981 0.3268
## Platelets 8.906e-03 1.009e+00 6.147e-03 1.449 0.1474
## Prothrombin -1.312e-01 8.770e-01 8.286e-01 -0.158 0.8742
## Stage2 2.556e+01 1.264e+11 6.556e+03 0.004 0.9969
## Stage3 2.705e+01 5.603e+11 6.556e+03 0.004 0.9967
## Stage4 2.706e+01 5.652e+11 6.556e+03 0.004 0.9967
## Z_star NA NA 0.000e+00 NA NA
## Age:Z_star -6.336e-06 1.000e+00 1.841e-05 -0.344 0.7307
## SexM:Z_star -3.343e-02 9.671e-01 1.199e-01 -0.279 0.7804
## AscitesY:Z_star -4.856e-01 6.153e-01 2.162e-01 -2.247 0.0247 *
## SpidersY:Z_star 3.885e-02 1.040e+00 7.754e-02 0.501 0.6164
## Alk_Phos:Z_star -1.489e-05 1.000e+00 1.141e-05 -1.305 0.1920
## SGOT:Z_star -9.775e-05 9.999e-01 4.856e-04 -0.201 0.8405
## Platelets:Z_star -4.856e-04 9.995e-01 3.835e-04 -1.266 0.2055
## Prothrombin:Z_star 2.991e-02 1.030e+00 5.039e-02 0.593 0.5529
## Stage2:Z_star -1.494e+00 2.244e-01 3.857e+02 -0.004 0.9969
## Stage3:Z_star -1.565e+00 2.090e-01 3.857e+02 -0.004 0.9968
## Stage4:Z_star -1.558e+00 2.106e-01 3.857e+02 -0.004 0.9968
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Age 1.000e+00 9.998e-01 0.99962 1.001e+00
## SexM 2.003e+00 4.993e-01 0.04546 8.824e+01
## AscitesY 6.934e+03 1.442e-04 5.96469 8.061e+06
## SpidersY 9.523e-01 1.050e+00 0.08348 1.086e+01
## Alk_Phos 1.000e+00 9.997e-01 0.99993 1.001e+00
## SGOT 1.007e+00 9.927e-01 0.99269 1.022e+00
## Platelets 1.009e+00 9.911e-01 0.99686 1.021e+00
## Prothrombin 8.770e-01 1.140e+00 0.17286 4.450e+00
## Stage2 1.264e+11 7.912e-12 0.00000 Inf
## Stage3 5.603e+11 1.785e-12 0.00000 Inf
## Stage4 5.652e+11 1.769e-12 0.00000 Inf
## Z_star NA NA NA NA
## Age:Z_star 1.000e+00 1.000e+00 0.99996 1.000e+00
## SexM:Z_star 9.671e-01 1.034e+00 0.76460 1.223e+00
## AscitesY:Z_star 6.153e-01 1.625e+00 0.40280 9.399e-01
## SpidersY:Z_star 1.040e+00 9.619e-01 0.89304 1.210e+00
## Alk_Phos:Z_star 1.000e+00 1.000e+00 0.99996 1.000e+00
## SGOT:Z_star 9.999e-01 1.000e+00 0.99895 1.001e+00
## Platelets:Z_star 9.995e-01 1.000e+00 0.99876 1.000e+00
## Prothrombin:Z_star 1.030e+00 9.705e-01 0.93346 1.137e+00
## Stage2:Z_star 2.244e-01 4.457e+00 0.00000 Inf
## Stage3:Z_star 2.090e-01 4.785e+00 0.00000 Inf
## Stage4:Z_star 2.106e-01 4.749e+00 0.00000 Inf
##
## Concordance= 0.737 (se = 0.03 )
## Likelihood ratio test= 58.96 on 22 df, p=3e-05
## Wald test = 22.01 on 22 df, p=0.5
## Score (logrank) test = 64.28 on 22 df, p=5e-06Model Strata Int 2 : Tanpa drug,sex dan interaksinya
strata.int2 = coxph(y ~ Age + Ascites + Spiders + Alk_Phos + SGOT + Platelets + Prothrombin + Stage
+ Age*Z_star + Ascites*Z_star + Spiders*Z_star + Alk_Phos*Z_star + SGOT*Z_star + Platelets*Z_star + Prothrombin*Z_star + Stage*Z_star +
strata(Z_star),
data = df)## Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
## Loglik converged before variable 8,9,10,19,20,21 ; coefficient may be infinite.summary(strata.int2)## Call:
## coxph(formula = y ~ Age + Ascites + Spiders + Alk_Phos + SGOT +
## Platelets + Prothrombin + Stage + Age * Z_star + Ascites *
## Z_star + Spiders * Z_star + Alk_Phos * Z_star + SGOT * Z_star +
## Platelets * Z_star + Prothrombin * Z_star + Stage * Z_star +
## strata(Z_star), data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Age 1.949e-04 1.000e+00 2.949e-04 0.661 0.5087
## AscitesY 8.407e+00 4.477e+03 3.525e+00 2.385 0.0171 *
## SpidersY -9.784e-02 9.068e-01 1.239e+00 -0.079 0.9371
## Alk_Phos 2.789e-04 1.000e+00 1.809e-04 1.541 0.1232
## SGOT 6.336e-03 1.006e+00 7.323e-03 0.865 0.3870
## Platelets 8.765e-03 1.009e+00 6.072e-03 1.443 0.1489
## Prothrombin -4.729e-02 9.538e-01 8.126e-01 -0.058 0.9536
## Stage2 2.571e+01 1.458e+11 6.702e+03 0.004 0.9969
## Stage3 2.724e+01 6.777e+11 6.702e+03 0.004 0.9968
## Stage4 2.744e+01 8.268e+11 6.702e+03 0.004 0.9967
## Z_star NA NA 0.000e+00 NA NA
## Age:Z_star -5.547e-06 1.000e+00 1.824e-05 -0.304 0.7610
## AscitesY:Z_star -4.589e-01 6.320e-01 2.114e-01 -2.171 0.0300 *
## SpidersY:Z_star 4.011e-02 1.041e+00 7.725e-02 0.519 0.6036
## Alk_Phos:Z_star -1.432e-05 1.000e+00 1.144e-05 -1.251 0.2108
## SGOT:Z_star -3.781e-05 1.000e+00 4.774e-04 -0.079 0.9369
## Platelets:Z_star -4.827e-04 9.995e-01 3.772e-04 -1.280 0.2006
## Prothrombin:Z_star 2.465e-02 1.025e+00 4.943e-02 0.499 0.6181
## Stage2:Z_star -1.507e+00 2.216e-01 3.942e+02 -0.004 0.9970
## Stage3:Z_star -1.580e+00 2.060e-01 3.942e+02 -0.004 0.9968
## Stage4:Z_star -1.584e+00 2.051e-01 3.942e+02 -0.004 0.9968
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Age 1.000e+00 9.998e-01 0.9996 1.001e+00
## AscitesY 4.477e+03 2.234e-04 4.4745 4.479e+06
## SpidersY 9.068e-01 1.103e+00 0.0799 1.029e+01
## Alk_Phos 1.000e+00 9.997e-01 0.9999 1.001e+00
## SGOT 1.006e+00 9.937e-01 0.9920 1.021e+00
## Platelets 1.009e+00 9.913e-01 0.9969 1.021e+00
## Prothrombin 9.538e-01 1.048e+00 0.1940 4.689e+00
## Stage2 1.458e+11 6.858e-12 0.0000 Inf
## Stage3 6.777e+11 1.476e-12 0.0000 Inf
## Stage4 8.268e+11 1.209e-12 0.0000 Inf
## Z_star NA NA NA NA
## Age:Z_star 1.000e+00 1.000e+00 1.0000 1.000e+00
## AscitesY:Z_star 6.320e-01 1.582e+00 0.4176 9.565e-01
## SpidersY:Z_star 1.041e+00 9.607e-01 0.8947 1.211e+00
## Alk_Phos:Z_star 1.000e+00 1.000e+00 1.0000 1.000e+00
## SGOT:Z_star 1.000e+00 1.000e+00 0.9990 1.001e+00
## Platelets:Z_star 9.995e-01 1.000e+00 0.9988 1.000e+00
## Prothrombin:Z_star 1.025e+00 9.757e-01 0.9303 1.129e+00
## Stage2:Z_star 2.216e-01 4.512e+00 0.0000 Inf
## Stage3:Z_star 2.060e-01 4.853e+00 0.0000 Inf
## Stage4:Z_star 2.051e-01 4.876e+00 0.0000 Inf
##
## Concordance= 0.739 (se = 0.03 )
## Likelihood ratio test= 58.62 on 20 df, p=1e-05
## Wald test = 21.6 on 20 df, p=0.4
## Score (logrank) test = 63.88 on 20 df, p=2e-06Model Strata Int 3 : Tanpa drug,sex,age, dan interaksinya
strata.int3 = coxph(y ~ Ascites + Spiders + Alk_Phos + SGOT + Platelets + Prothrombin + Stage
+ Ascites*Z_star + Spiders*Z_star + Alk_Phos*Z_star + SGOT*Z_star + Platelets*Z_star + Prothrombin*Z_star + Stage*Z_star +
strata(Z_star),
data = df) ## Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
## Loglik converged before variable 7,8,9,17,18,19 ; coefficient may be infinite.summary(strata.int3)## Call:
## coxph(formula = y ~ Ascites + Spiders + Alk_Phos + SGOT + Platelets +
## Prothrombin + Stage + Ascites * Z_star + Spiders * Z_star +
## Alk_Phos * Z_star + SGOT * Z_star + Platelets * Z_star +
## Prothrombin * Z_star + Stage * Z_star + strata(Z_star), data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## AscitesY 7.497e+00 1.802e+03 3.462e+00 2.165 0.0304 *
## SpidersY -3.735e-01 6.883e-01 1.198e+00 -0.312 0.7552
## Alk_Phos 3.312e-04 1.000e+00 1.751e-04 1.892 0.0585 .
## SGOT 6.344e-03 1.006e+00 7.374e-03 0.860 0.3896
## Platelets 7.918e-03 1.008e+00 5.835e-03 1.357 0.1748
## Prothrombin 2.780e-03 1.003e+00 8.057e-01 0.003 0.9972
## Stage2 2.525e+01 9.290e+10 6.525e+03 0.004 0.9969
## Stage3 2.712e+01 5.996e+11 6.525e+03 0.004 0.9967
## Stage4 2.735e+01 7.527e+11 6.525e+03 0.004 0.9967
## Z_star NA NA 0.000e+00 NA NA
## AscitesY:Z_star -4.056e-01 6.666e-01 2.078e-01 -1.951 0.0510 .
## SpidersY:Z_star 5.860e-02 1.060e+00 7.473e-02 0.784 0.4329
## Alk_Phos:Z_star -1.721e-05 1.000e+00 1.115e-05 -1.544 0.1227
## SGOT:Z_star -8.106e-05 9.999e-01 4.785e-04 -0.169 0.8655
## Platelets:Z_star -4.626e-04 9.995e-01 3.614e-04 -1.280 0.2006
## Prothrombin:Z_star 2.259e-02 1.023e+00 4.895e-02 0.461 0.6445
## Stage2:Z_star -1.483e+00 2.270e-01 3.838e+02 -0.004 0.9969
## Stage3:Z_star -1.574e+00 2.073e-01 3.838e+02 -0.004 0.9967
## Stage4:Z_star -1.576e+00 2.067e-01 3.838e+02 -0.004 0.9967
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## AscitesY 1.802e+03 5.550e-04 2.03460 1.596e+06
## SpidersY 6.883e-01 1.453e+00 0.06574 7.206e+00
## Alk_Phos 1.000e+00 9.997e-01 0.99999 1.001e+00
## SGOT 1.006e+00 9.937e-01 0.99192 1.021e+00
## Platelets 1.008e+00 9.921e-01 0.99649 1.020e+00
## Prothrombin 1.003e+00 9.972e-01 0.20672 4.865e+00
## Stage2 9.290e+10 1.076e-11 0.00000 Inf
## Stage3 5.996e+11 1.668e-12 0.00000 Inf
## Stage4 7.527e+11 1.328e-12 0.00000 Inf
## Z_star NA NA NA NA
## AscitesY:Z_star 6.666e-01 1.500e+00 0.44357 1.002e+00
## SpidersY:Z_star 1.060e+00 9.431e-01 0.91589 1.228e+00
## Alk_Phos:Z_star 1.000e+00 1.000e+00 0.99996 1.000e+00
## SGOT:Z_star 9.999e-01 1.000e+00 0.99898 1.001e+00
## Platelets:Z_star 9.995e-01 1.000e+00 0.99883 1.000e+00
## Prothrombin:Z_star 1.023e+00 9.777e-01 0.92926 1.126e+00
## Stage2:Z_star 2.270e-01 4.406e+00 0.00000 Inf
## Stage3:Z_star 2.073e-01 4.825e+00 0.00000 Inf
## Stage4:Z_star 2.067e-01 4.838e+00 0.00000 Inf
##
## Concordance= 0.72 (se = 0.033 )
## Likelihood ratio test= 55.82 on 18 df, p=1e-05
## Wald test = 22.76 on 18 df, p=0.2
## Score (logrank) test = 59.77 on 18 df, p=2e-06Model Strata Int 4 : Tanpa drug,sex,age,alk_phos dan interaksinya
strata.int4 = coxph(y ~ Ascites + Spiders + SGOT + Platelets + Prothrombin + Stage
+ Ascites*Z_star + Spiders*Z_star + SGOT*Z_star + Platelets*Z_star + Prothrombin*Z_star + Stage*Z_star +
strata(Z_star),
data = df)## Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
## Loglik converged before variable 6,7,8,15,16,17 ; coefficient may be infinite.summary(strata.int4)## Call:
## coxph(formula = y ~ Ascites + Spiders + SGOT + Platelets + Prothrombin +
## Stage + Ascites * Z_star + Spiders * Z_star + SGOT * Z_star +
## Platelets * Z_star + Prothrombin * Z_star + Stage * Z_star +
## strata(Z_star), data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## AscitesY 7.916e+00 2.742e+03 3.492e+00 2.267 0.0234 *
## SpidersY 3.963e-01 1.486e+00 1.134e+00 0.350 0.7266
## SGOT 5.259e-03 1.005e+00 7.426e-03 0.708 0.4788
## Platelets 1.042e-02 1.010e+00 5.863e-03 1.778 0.0755 .
## Prothrombin -3.842e-02 9.623e-01 7.658e-01 -0.050 0.9600
## Stage2 2.520e+01 8.766e+10 5.392e+03 0.005 0.9963
## Stage3 2.661e+01 3.599e+11 5.392e+03 0.005 0.9961
## Stage4 2.690e+01 4.818e+11 5.392e+03 0.005 0.9960
## Z_star NA NA 0.000e+00 NA NA
## AscitesY:Z_star -4.273e-01 6.522e-01 2.095e-01 -2.040 0.0414 *
## SpidersY:Z_star 1.078e-02 1.011e+00 7.102e-02 0.152 0.8793
## SGOT:Z_star -1.460e-05 1.000e+00 4.805e-04 -0.030 0.9758
## Platelets:Z_star -5.986e-04 9.994e-01 3.628e-04 -1.650 0.0989 .
## Prothrombin:Z_star 2.570e-02 1.026e+00 4.673e-02 0.550 0.5823
## Stage2:Z_star -1.475e+00 2.288e-01 3.172e+02 -0.005 0.9963
## Stage3:Z_star -1.539e+00 2.147e-01 3.172e+02 -0.005 0.9961
## Stage4:Z_star -1.549e+00 2.125e-01 3.172e+02 -0.005 0.9961
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## AscitesY 2.742e+03 3.647e-04 2.920e+00 2.575e+06
## SpidersY 1.486e+00 6.728e-01 1.611e-01 1.371e+01
## SGOT 1.005e+00 9.948e-01 9.907e-01 1.020e+00
## Platelets 1.010e+00 9.896e-01 9.989e-01 1.022e+00
## Prothrombin 9.623e-01 1.039e+00 2.145e-01 4.317e+00
## Stage2 8.766e+10 1.141e-11 0.000e+00 Inf
## Stage3 3.599e+11 2.779e-12 0.000e+00 Inf
## Stage4 4.818e+11 2.075e-12 0.000e+00 Inf
## Z_star NA NA NA NA
## AscitesY:Z_star 6.522e-01 1.533e+00 4.326e-01 9.834e-01
## SpidersY:Z_star 1.011e+00 9.893e-01 8.795e-01 1.162e+00
## SGOT:Z_star 1.000e+00 1.000e+00 9.990e-01 1.001e+00
## Platelets:Z_star 9.994e-01 1.001e+00 9.987e-01 1.000e+00
## Prothrombin:Z_star 1.026e+00 9.746e-01 9.362e-01 1.124e+00
## Stage2:Z_star 2.288e-01 4.371e+00 2.336e-271 2.241e+269
## Stage3:Z_star 2.147e-01 4.658e+00 2.192e-271 2.103e+269
## Stage4:Z_star 2.125e-01 4.706e+00 2.170e-271 2.081e+269
##
## Concordance= 0.722 (se = 0.032 )
## Likelihood ratio test= 50.93 on 16 df, p=2e-05
## Wald test = 41.56 on 16 df, p=5e-04
## Score (logrank) test = 52.05 on 16 df, p=1e-05Model Strata Int 5 : Tanpa drug,sex,age,alk_phos,platelets dan interaksinya
strata.int5 = coxph(y ~ Ascites + Spiders + SGOT + Prothrombin + Stage
+ Ascites*Z_star + Spiders*Z_star + SGOT*Z_star + Prothrombin*Z_star + Stage*Z_star +
strata(Z_star),
data = df) ## Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
## Loglik converged before variable 5,6,7,13,14,15 ; coefficient may be infinite.summary(strata.int5)## Call:
## coxph(formula = y ~ Ascites + Spiders + SGOT + Prothrombin +
## Stage + Ascites * Z_star + Spiders * Z_star + SGOT * Z_star +
## Prothrombin * Z_star + Stage * Z_star + strata(Z_star), data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## AscitesY 7.246e+00 1.403e+03 3.468e+00 2.089 0.0367 *
## SpidersY 3.288e-01 1.389e+00 1.104e+00 0.298 0.7657
## SGOT 4.893e-03 1.005e+00 7.198e-03 0.680 0.4967
## Prothrombin -2.822e-02 9.722e-01 7.786e-01 -0.036 0.9711
## Stage2 2.694e+01 4.992e+11 5.522e+03 0.005 0.9961
## Stage3 2.779e+01 1.174e+12 5.522e+03 0.005 0.9960
## Stage4 2.822e+01 1.809e+12 5.522e+03 0.005 0.9959
## Z_star NA NA 0.000e+00 NA NA
## AscitesY:Z_star -3.877e-01 6.786e-01 2.080e-01 -1.864 0.0623 .
## SpidersY:Z_star 1.433e-02 1.014e+00 6.932e-02 0.207 0.8362
## SGOT:Z_star 9.526e-06 1.000e+00 4.661e-04 0.020 0.9837
## Prothrombin:Z_star 2.519e-02 1.026e+00 4.743e-02 0.531 0.5953
## Stage2:Z_star -1.579e+00 2.062e-01 3.248e+02 -0.005 0.9961
## Stage3:Z_star -1.611e+00 1.998e-01 3.248e+02 -0.005 0.9960
## Stage4:Z_star -1.629e+00 1.961e-01 3.248e+02 -0.005 0.9960
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## AscitesY 1.403e+03 7.128e-04 1.566e+00 1.257e+06
## SpidersY 1.389e+00 7.198e-01 1.598e-01 1.208e+01
## SGOT 1.005e+00 9.951e-01 9.908e-01 1.019e+00
## Prothrombin 9.722e-01 1.029e+00 2.113e-01 4.472e+00
## Stage2 4.992e+11 2.003e-12 0.000e+00 Inf
## Stage3 1.174e+12 8.519e-13 0.000e+00 Inf
## Stage4 1.809e+12 5.528e-13 0.000e+00 Inf
## Z_star NA NA NA NA
## AscitesY:Z_star 6.786e-01 1.474e+00 4.514e-01 1.020e+00
## SpidersY:Z_star 1.014e+00 9.858e-01 8.856e-01 1.162e+00
## SGOT:Z_star 1.000e+00 1.000e+00 9.991e-01 1.001e+00
## Prothrombin:Z_star 1.026e+00 9.751e-01 9.345e-01 1.125e+00
## Stage2:Z_star 2.062e-01 4.850e+00 6.814e-278 6.239e+275
## Stage3:Z_star 1.998e-01 5.005e+00 6.603e-278 6.045e+275
## Stage4:Z_star 1.961e-01 5.099e+00 6.481e-278 5.934e+275
##
## Concordance= 0.72 (se = 0.032 )
## Likelihood ratio test= 47.59 on 14 df, p=2e-05
## Wald test = 39.14 on 14 df, p=3e-04
## Score (logrank) test = 48.84 on 14 df, p=1e-05Model Strata Int 6 : Tanpa drug,sex,age,alk_phos,platelets,stage dan interaksinya
strata.int6 = coxph(y ~ Ascites + Spiders + SGOT + Prothrombin +
+ Ascites*Z_star + Spiders*Z_star + SGOT*Z_star + Prothrombin*Z_star +
strata(Z_star),
data = df)
summary(strata.int6)## Call:
## coxph(formula = y ~ Ascites + Spiders + SGOT + Prothrombin +
## +Ascites * Z_star + Spiders * Z_star + SGOT * Z_star + Prothrombin *
## Z_star + strata(Z_star), data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## AscitesY 7.452e+00 1.724e+03 3.474e+00 2.145 0.0319 *
## SpidersY 7.513e-01 2.120e+00 1.057e+00 0.711 0.4772
## SGOT 7.866e-03 1.008e+00 7.010e-03 1.122 0.2618
## Prothrombin -1.251e-01 8.824e-01 7.155e-01 -0.175 0.8612
## Z_star NA NA 0.000e+00 NA NA
## AscitesY:Z_star -3.973e-01 6.721e-01 2.080e-01 -1.910 0.0561 .
## SpidersY:Z_star -6.136e-03 9.939e-01 6.640e-02 -0.092 0.9264
## SGOT:Z_star -2.051e-04 9.998e-01 4.511e-04 -0.455 0.6493
## Prothrombin:Z_star 3.275e-02 1.033e+00 4.363e-02 0.751 0.4529
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## AscitesY 1724.1626 0.00058 1.9034 1.562e+06
## SpidersY 2.1198 0.47174 0.2670 1.683e+01
## SGOT 1.0079 0.99216 0.9941 1.022e+00
## Prothrombin 0.8824 1.13326 0.2171 3.587e+00
## Z_star NA NA NA NA
## AscitesY:Z_star 0.6721 1.48778 0.4471 1.010e+00
## SpidersY:Z_star 0.9939 1.00615 0.8726 1.132e+00
## SGOT:Z_star 0.9998 1.00021 0.9989 1.001e+00
## Prothrombin:Z_star 1.0333 0.96778 0.9486 1.126e+00
##
## Concordance= 0.706 (se = 0.034 )
## Likelihood ratio test= 43.57 on 8 df, p=7e-07
## Wald test = 40.49 on 8 df, p=3e-06
## Score (logrank) test = 45.63 on 8 df, p=3e-07Model Strata Int 7 : Tanpa interaksi spiders*zstar
strata.int7 = coxph(y ~ Ascites + Spiders + SGOT + Prothrombin +
Ascites*Z_star + SGOT*Z_star + Prothrombin*Z_star +
strata(Z_star),
data = df)
summary(strata.int7)## Call:
## coxph(formula = y ~ Ascites + Spiders + SGOT + Prothrombin +
## Ascites * Z_star + SGOT * Z_star + Prothrombin * Z_star +
## strata(Z_star), data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## AscitesY 7.451e+00 1.722e+03 3.472e+00 2.146 0.03186 *
## SpidersY 6.557e-01 1.927e+00 2.210e-01 2.967 0.00301 **
## SGOT 7.837e-03 1.008e+00 6.993e-03 1.121 0.26245
## Prothrombin -1.140e-01 8.922e-01 7.046e-01 -0.162 0.87145
## Z_star NA NA 0.000e+00 NA NA
## AscitesY:Z_star -3.972e-01 6.722e-01 2.079e-01 -1.911 0.05600 .
## SGOT:Z_star -2.037e-04 9.998e-01 4.503e-04 -0.452 0.65103
## Prothrombin:Z_star 3.208e-02 1.033e+00 4.298e-02 0.746 0.45542
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## AscitesY 1721.7305 0.0005808 1.9090 1.553e+06
## SpidersY 1.9266 0.5190565 1.2492 2.971e+00
## SGOT 1.0079 0.9921940 0.9941 1.022e+00
## Prothrombin 0.8922 1.1207622 0.2243 3.550e+00
## Z_star NA NA NA NA
## AscitesY:Z_star 0.6722 1.4876936 0.4473 1.010e+00
## SGOT:Z_star 0.9998 1.0002037 0.9989 1.001e+00
## Prothrombin:Z_star 1.0326 0.9684270 0.9492 1.123e+00
##
## Concordance= 0.707 (se = 0.034 )
## Likelihood ratio test= 43.56 on 7 df, p=3e-07
## Wald test = 40.5 on 7 df, p=1e-06
## Score (logrank) test = 45.63 on 7 df, p=1e-07Model Strata Int 8 : Tanpa interkasi spiders dan sgot dengan zstar
strata.int8 = coxph(y ~ Ascites + Spiders + SGOT + Prothrombin +
Ascites*Z_star + Prothrombin*Z_star +
strata(Z_star),
data = df)
summary(strata.int8)## Call:
## coxph(formula = y ~ Ascites + Spiders + SGOT + Prothrombin +
## Ascites * Z_star + Prothrombin * Z_star + strata(Z_star),
## data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## AscitesY 7.378e+00 1.601e+03 3.475e+00 2.124 0.03371 *
## SpidersY 6.511e-01 1.918e+00 2.207e-01 2.950 0.00318 **
## SGOT 4.743e-03 1.005e+00 1.617e-03 2.932 0.00337 **
## Prothrombin -1.419e-01 8.677e-01 6.922e-01 -0.205 0.83760
## Z_star NA NA 0.000e+00 NA NA
## AscitesY:Z_star -3.929e-01 6.751e-01 2.080e-01 -1.889 0.05889 .
## Prothrombin:Z_star 3.423e-02 1.035e+00 4.217e-02 0.812 0.41701
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## AscitesY 1600.8003 0.0006247 1.7651 1.452e+06
## SpidersY 1.9176 0.5214802 1.2442 2.956e+00
## SGOT 1.0048 0.9952686 1.0016 1.008e+00
## Prothrombin 0.8677 1.1524312 0.2235 3.369e+00
## Z_star NA NA NA NA
## AscitesY:Z_star 0.6751 1.4813337 0.4490 1.015e+00
## Prothrombin:Z_star 1.0348 0.9663494 0.9527 1.124e+00
##
## Concordance= 0.706 (se = 0.034 )
## Likelihood ratio test= 43.37 on 6 df, p=1e-07
## Wald test = 40.26 on 6 df, p=4e-07
## Score (logrank) test = 45.42 on 6 df, p=4e-08Model Strata Int 9 : Tanpa interkasi spiders,sgot,proth dengan zstar
strata.int9 = coxph(y ~ Ascites + Spiders + SGOT + Prothrombin +
Ascites*Z_star +
strata(Z_star),
data = df)
summary(strata.int9)## Call:
## coxph(formula = y ~ Ascites + Spiders + SGOT + Prothrombin +
## Ascites * Z_star + strata(Z_star), data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## AscitesY 7.386e+00 1.613e+03 3.424e+00 2.157 0.030979 *
## SpidersY 6.351e-01 1.887e+00 2.199e-01 2.888 0.003883 **
## SGOT 4.663e-03 1.005e+00 1.614e-03 2.890 0.003856 **
## Prothrombin 4.104e-01 1.507e+00 1.238e-01 3.314 0.000918 ***
## Z_star NA NA 0.000e+00 NA NA
## AscitesY:Z_star -3.901e-01 6.770e-01 2.052e-01 -1.901 0.057237 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## AscitesY 1613.134 0.0006199 1.9655 1.324e+06
## SpidersY 1.887 0.5298794 1.2263 2.904e+00
## SGOT 1.005 0.9953479 1.0015 1.008e+00
## Prothrombin 1.507 0.6633530 1.1826 1.922e+00
## Z_star NA NA NA NA
## AscitesY:Z_star 0.677 1.4771574 0.4528 1.012e+00
##
## Concordance= 0.704 (se = 0.034 )
## Likelihood ratio test= 42.7 on 5 df, p=4e-08
## Wald test = 39.29 on 5 df, p=2e-07
## Score (logrank) test = 44.42 on 5 df, p=2e-08Model Strata Int 10 : Tanpa ascites hanya interaksinya saja
strata.int10 = coxph(y ~ Spiders + SGOT + Prothrombin +
Ascites*Z_star +
strata(Z_star),
data = df)
summary(strata.int10)## Call:
## coxph(formula = y ~ Spiders + SGOT + Prothrombin + Ascites *
## Z_star + strata(Z_star), data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## SpidersY 6.351e-01 1.887e+00 2.199e-01 2.888 0.003883 **
## SGOT 4.663e-03 1.005e+00 1.614e-03 2.890 0.003856 **
## Prothrombin 4.104e-01 1.507e+00 1.238e-01 3.314 0.000918 ***
## AscitesY 7.386e+00 1.613e+03 3.424e+00 2.157 0.030979 *
## Z_star NA NA 0.000e+00 NA NA
## AscitesY:Z_star -3.901e-01 6.770e-01 2.052e-01 -1.901 0.057237 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SpidersY 1.887 0.5298794 1.2263 2.904e+00
## SGOT 1.005 0.9953479 1.0015 1.008e+00
## Prothrombin 1.507 0.6633530 1.1826 1.922e+00
## AscitesY 1613.134 0.0006199 1.9655 1.324e+06
## Z_star NA NA NA NA
## AscitesY:Z_star 0.677 1.4771574 0.4528 1.012e+00
##
## Concordance= 0.704 (se = 0.034 )
## Likelihood ratio test= 42.7 on 5 df, p=4e-08
## Wald test = 39.29 on 5 df, p=2e-07
## Score (logrank) test = 44.42 on 5 df, p=2e-08Perbandingan : Manakah model strata dengan interaksi yang terbaik?
interaction_model_list = list(
strata.int_full = strata.int_full,
strata.int1 = strata.int1,
strata.int2 = strata.int2,
strata.int3 = strata.int3,
strata.int4 = strata.int4,
strata.int5 = strata.int5,
strata.int6 = strata.int6,
strata.int7 = strata.int7,
strata.int8 = strata.int8,
strata.int9 = strata.int9,
strata.int10 = strata.int10
)
stra.int_summary = data.frame()
for (model_name in names(interaction_model_list)) {
model = interaction_model_list[[model_name]]
model_summary_data = summary(model)
variables = rownames(model_summary_data$coefficients)
HR = model_summary_data$coefficients[, "exp(coef)"]
CI_lower = model_summary_data$conf.int[, "lower .95"]
CI_upper = model_summary_data$conf.int[, "upper .95"]
model_AIC = AIC(model)
temp_df = data.frame(
Model = model_name,
Variable = variables,
HR = HR,
CI_Lower = CI_lower,
CI_Upper = CI_upper,
AIC = model_AIC
)
stra.int_summary = rbind(stra.int_summary, temp_df)
}
# Print hasil akhir
print(stra.int_summary)## Model Variable HR
## DrugPlacebo strata.int_full DrugPlacebo 2.972012e-01
## Age strata.int_full Age 1.000168e+00
## SexM strata.int_full SexM 2.114988e+00
## AscitesY strata.int_full AscitesY 1.155380e+04
## SpidersY strata.int_full SpidersY 9.461361e-01
## Alk_Phos strata.int_full Alk_Phos 1.000269e+00
## SGOT strata.int_full SGOT 1.008601e+00
## Platelets strata.int_full Platelets 1.008056e+00
## Prothrombin strata.int_full Prothrombin 1.238109e+00
## Stage2 strata.int_full Stage2 2.628239e+11
## Stage3 strata.int_full Stage3 9.878790e+11
## Stage4 strata.int_full Stage4 9.841466e+11
## Z_star strata.int_full Z_star NA
## DrugPlacebo:Z_star strata.int_full DrugPlacebo:Z_star 1.083278e+00
## Age:Z_star strata.int_full Age:Z_star 9.999961e-01
## SexM:Z_star strata.int_full SexM:Z_star 9.647826e-01
## AscitesY:Z_star strata.int_full AscitesY:Z_star 5.985060e-01
## SpidersY:Z_star strata.int_full SpidersY:Z_star 1.041599e+00
## Alk_Phos:Z_star strata.int_full Alk_Phos:Z_star 9.999862e-01
## SGOT:Z_star strata.int_full SGOT:Z_star 9.998124e-01
## Platelets:Z_star strata.int_full Platelets:Z_star 9.995647e-01
## Prothrombin:Z_star strata.int_full Prothrombin:Z_star 1.008879e+00
## Stage2:Z_star strata.int_full Stage2:Z_star 2.141616e-01
## Stage3:Z_star strata.int_full Stage3:Z_star 2.011257e-01
## Stage4:Z_star strata.int_full Stage4:Z_star 2.023781e-01
## Age1 strata.int1 Age 1.000206e+00
## SexM1 strata.int1 SexM 2.002912e+00
## AscitesY1 strata.int1 AscitesY 6.933900e+03
## SpidersY1 strata.int1 SpidersY 9.523195e-01
## Alk_Phos1 strata.int1 Alk_Phos 1.000287e+00
## SGOT1 strata.int1 SGOT 1.007373e+00
## Platelets1 strata.int1 Platelets 1.008946e+00
## Prothrombin1 strata.int1 Prothrombin 8.770074e-01
## Stage21 strata.int1 Stage2 1.263974e+11
## Stage31 strata.int1 Stage3 5.602553e+11
## Stage41 strata.int1 Stage4 5.651942e+11
## Z_star1 strata.int1 Z_star NA
## Age:Z_star1 strata.int1 Age:Z_star 9.999937e-01
## SexM:Z_star1 strata.int1 SexM:Z_star 9.671237e-01
## AscitesY:Z_star1 strata.int1 AscitesY:Z_star 6.153077e-01
## SpidersY:Z_star1 strata.int1 SpidersY:Z_star 1.039613e+00
## Alk_Phos:Z_star1 strata.int1 Alk_Phos:Z_star 9.999851e-01
## SGOT:Z_star1 strata.int1 SGOT:Z_star 9.999023e-01
## Platelets:Z_star1 strata.int1 Platelets:Z_star 9.995146e-01
## Prothrombin:Z_star1 strata.int1 Prothrombin:Z_star 1.030358e+00
## Stage2:Z_star1 strata.int1 Stage2:Z_star 2.243694e-01
## Stage3:Z_star1 strata.int1 Stage3:Z_star 2.089916e-01
## Stage4:Z_star1 strata.int1 Stage4:Z_star 2.105711e-01
## Age2 strata.int2 Age 1.000195e+00
## AscitesY2 strata.int2 AscitesY 4.476680e+03
## SpidersY2 strata.int2 SpidersY 9.067968e-01
## Alk_Phos2 strata.int2 Alk_Phos 1.000279e+00
## SGOT2 strata.int2 SGOT 1.006356e+00
## Platelets2 strata.int2 Platelets 1.008803e+00
## Prothrombin2 strata.int2 Prothrombin 9.538151e-01
## Stage22 strata.int2 Stage2 1.458159e+11
## Stage32 strata.int2 Stage3 6.777161e+11
## Stage42 strata.int2 Stage4 8.268488e+11
## Z_star2 strata.int2 Z_star NA
## Age:Z_star2 strata.int2 Age:Z_star 9.999945e-01
## AscitesY:Z_star2 strata.int2 AscitesY:Z_star 6.319692e-01
## SpidersY:Z_star2 strata.int2 SpidersY:Z_star 1.040924e+00
## Alk_Phos:Z_star2 strata.int2 Alk_Phos:Z_star 9.999857e-01
## SGOT:Z_star2 strata.int2 SGOT:Z_star 9.999622e-01
## Platelets:Z_star2 strata.int2 Platelets:Z_star 9.995174e-01
## Prothrombin:Z_star2 strata.int2 Prothrombin:Z_star 1.024951e+00
## Stage2:Z_star2 strata.int2 Stage2:Z_star 2.216299e-01
## Stage3:Z_star2 strata.int2 Stage3:Z_star 2.060379e-01
## Stage4:Z_star2 strata.int2 Stage4:Z_star 2.051070e-01
## AscitesY3 strata.int3 AscitesY 1.801912e+03
## SpidersY3 strata.int3 SpidersY 6.883091e-01
## Alk_Phos3 strata.int3 Alk_Phos 1.000331e+00
## SGOT3 strata.int3 SGOT 1.006364e+00
## Platelets3 strata.int3 Platelets 1.007949e+00
## Prothrombin3 strata.int3 Prothrombin 1.002784e+00
## Stage23 strata.int3 Stage2 9.290260e+10
## Stage33 strata.int3 Stage3 5.996304e+11
## Stage43 strata.int3 Stage4 7.527457e+11
## Z_star3 strata.int3 Z_star NA
## AscitesY:Z_star3 strata.int3 AscitesY:Z_star 6.666066e-01
## SpidersY:Z_star3 strata.int3 SpidersY:Z_star 1.060353e+00
## Alk_Phos:Z_star3 strata.int3 Alk_Phos:Z_star 9.999828e-01
## SGOT:Z_star3 strata.int3 SGOT:Z_star 9.999189e-01
## Platelets:Z_star3 strata.int3 Platelets:Z_star 9.995376e-01
## Prothrombin:Z_star3 strata.int3 Prothrombin:Z_star 1.022842e+00
## Stage2:Z_star3 strata.int3 Stage2:Z_star 2.269827e-01
## Stage3:Z_star3 strata.int3 Stage3:Z_star 2.072508e-01
## Stage4:Z_star3 strata.int3 Stage4:Z_star 2.067081e-01
## AscitesY4 strata.int4 AscitesY 2.741760e+03
## SpidersY4 strata.int4 SpidersY 1.486349e+00
## SGOT4 strata.int4 SGOT 1.005273e+00
## Platelets4 strata.int4 Platelets 1.010477e+00
## Prothrombin4 strata.int4 Prothrombin 9.623041e-01
## Stage24 strata.int4 Stage2 8.765989e+10
## Stage34 strata.int4 Stage3 3.599030e+11
## Stage44 strata.int4 Stage4 4.818392e+11
## Z_star4 strata.int4 Z_star NA
## AscitesY:Z_star4 strata.int4 AscitesY:Z_star 6.522462e-01
## SpidersY:Z_star4 strata.int4 SpidersY:Z_star 1.010841e+00
## SGOT:Z_star4 strata.int4 SGOT:Z_star 9.999854e-01
## Platelets:Z_star4 strata.int4 Platelets:Z_star 9.994016e-01
## Prothrombin:Z_star4 strata.int4 Prothrombin:Z_star 1.026032e+00
## Stage2:Z_star4 strata.int4 Stage2:Z_star 2.287839e-01
## Stage3:Z_star4 strata.int4 Stage3:Z_star 2.146776e-01
## Stage4:Z_star4 strata.int4 Stage4:Z_star 2.125013e-01
## AscitesY5 strata.int5 AscitesY 1.402968e+03
## SpidersY5 strata.int5 SpidersY 1.389313e+00
## SGOT5 strata.int5 SGOT 1.004905e+00
## Prothrombin5 strata.int5 Prothrombin 9.721728e-01
## Stage25 strata.int5 Stage2 4.991521e+11
## Stage35 strata.int5 Stage3 1.173906e+12
## Stage45 strata.int5 Stage4 1.809024e+12
## Z_star5 strata.int5 Z_star NA
## AscitesY:Z_star5 strata.int5 AscitesY:Z_star 6.786340e-01
## SpidersY:Z_star5 strata.int5 SpidersY:Z_star 1.014432e+00
## SGOT:Z_star5 strata.int5 SGOT:Z_star 1.000010e+00
## Prothrombin:Z_star5 strata.int5 Prothrombin:Z_star 1.025513e+00
## Stage2:Z_star5 strata.int5 Stage2:Z_star 2.061837e-01
## Stage3:Z_star5 strata.int5 Stage3:Z_star 1.997853e-01
## Stage4:Z_star5 strata.int5 Stage4:Z_star 1.961196e-01
## AscitesY6 strata.int6 AscitesY 1.724163e+03
## SpidersY6 strata.int6 SpidersY 2.119825e+00
## SGOT6 strata.int6 SGOT 1.007897e+00
## Prothrombin6 strata.int6 Prothrombin 8.824064e-01
## Z_star6 strata.int6 Z_star NA
## AscitesY:Z_star6 strata.int6 AscitesY:Z_star 6.721424e-01
## SpidersY:Z_star6 strata.int6 SpidersY:Z_star 9.938830e-01
## SGOT:Z_star6 strata.int6 SGOT:Z_star 9.997949e-01
## Prothrombin:Z_star6 strata.int6 Prothrombin:Z_star 1.033289e+00
## AscitesY7 strata.int7 AscitesY 1.721730e+03
## SpidersY7 strata.int7 SpidersY 1.926573e+00
## SGOT7 strata.int7 SGOT 1.007867e+00
## Prothrombin7 strata.int7 Prothrombin 8.922499e-01
## Z_star7 strata.int7 Z_star NA
## AscitesY:Z_star7 strata.int7 AscitesY:Z_star 6.721814e-01
## SGOT:Z_star7 strata.int7 SGOT:Z_star 9.997963e-01
## Prothrombin:Z_star7 strata.int7 Prothrombin:Z_star 1.032602e+00
## AscitesY8 strata.int8 AscitesY 1.600800e+03
## SpidersY8 strata.int8 SpidersY 1.917618e+00
## SGOT8 strata.int8 SGOT 1.004754e+00
## Prothrombin8 strata.int8 Prothrombin 8.677308e-01
## Z_star8 strata.int8 Z_star NA
## AscitesY:Z_star8 strata.int8 AscitesY:Z_star 6.750673e-01
## Prothrombin:Z_star8 strata.int8 Prothrombin:Z_star 1.034822e+00
## AscitesY9 strata.int9 AscitesY 1.613134e+03
## SpidersY9 strata.int9 SpidersY 1.887222e+00
## SGOT9 strata.int9 SGOT 1.004674e+00
## Prothrombin9 strata.int9 Prothrombin 1.507493e+00
## Z_star9 strata.int9 Z_star NA
## AscitesY:Z_star9 strata.int9 AscitesY:Z_star 6.769759e-01
## SpidersY10 strata.int10 SpidersY 1.887222e+00
## SGOT10 strata.int10 SGOT 1.004674e+00
## Prothrombin10 strata.int10 Prothrombin 1.507493e+00
## AscitesY10 strata.int10 AscitesY 1.613134e+03
## Z_star10 strata.int10 Z_star NA
## AscitesY:Z_star10 strata.int10 AscitesY:Z_star 6.769759e-01
## CI_Lower CI_Upper AIC
## DrugPlacebo 4.764397e-02 1.853929e+00 606.5616
## Age 9.995677e-01 1.000768e+00 606.5616
## SexM 4.593096e-02 9.738912e+01 606.5616
## AscitesY 9.419885e+00 1.417112e+07 606.5616
## SpidersY 7.823957e-02 1.144144e+01 606.5616
## Alk_Phos 9.999080e-01 1.000630e+00 606.5616
## SGOT 9.929812e-01 1.024466e+00 606.5616
## Platelets 9.961788e-01 1.020075e+00 606.5616
## Prothrombin 2.189896e-01 6.999939e+00 606.5616
## Stage2 0.000000e+00 Inf 606.5616
## Stage3 0.000000e+00 Inf 606.5616
## Stage4 0.000000e+00 Inf 606.5616
## Z_star NA NA 606.5616
## DrugPlacebo:Z_star 9.620936e-01 1.219728e+00 606.5616
## Age:Z_star 9.999591e-01 1.000033e+00 606.5616
## SexM:Z_star 7.612757e-01 1.222692e+00 606.5616
## AscitesY:Z_star 3.906848e-01 9.168757e-01 606.5616
## SpidersY:Z_star 8.916782e-01 1.216726e+00 606.5616
## Alk_Phos:Z_star 9.999634e-01 1.000009e+00 606.5616
## SGOT:Z_star 9.988085e-01 1.000817e+00 606.5616
## Platelets:Z_star 9.988244e-01 1.000306e+00 606.5616
## Prothrombin:Z_star 9.082913e-01 1.120605e+00 606.5616
## Stage2:Z_star 0.000000e+00 Inf 606.5616
## Stage3:Z_star 0.000000e+00 Inf 606.5616
## Stage4:Z_star 0.000000e+00 Inf 606.5616
## Age1 9.996237e-01 1.000789e+00 604.3462
## SexM1 4.546360e-02 8.823888e+01 604.3462
## AscitesY1 5.964685e+00 8.060604e+06 604.3462
## SpidersY1 8.348442e-02 1.086325e+01 604.3462
## Alk_Phos1 9.999339e-01 1.000640e+00 604.3462
## SGOT1 9.926898e-01 1.022273e+00 604.3462
## Platelets1 9.968626e-01 1.021176e+00 604.3462
## Prothrombin1 1.728556e-01 4.449622e+00 604.3462
## Stage21 0.000000e+00 Inf 604.3462
## Stage31 0.000000e+00 Inf 604.3462
## Stage41 0.000000e+00 Inf 604.3462
## Z_star1 NA NA 604.3462
## Age:Z_star1 9.999576e-01 1.000030e+00 604.3462
## SexM:Z_star1 7.646008e-01 1.223290e+00 604.3462
## AscitesY:Z_star1 4.028037e-01 9.399205e-01 604.3462
## SpidersY:Z_star1 8.930395e-01 1.210242e+00 604.3462
## Alk_Phos:Z_star1 9.999627e-01 1.000007e+00 604.3462
## SGOT:Z_star1 9.989511e-01 1.000854e+00 604.3462
## Platelets:Z_star1 9.987635e-01 1.000266e+00 604.3462
## Prothrombin:Z_star1 9.334604e-01 1.137314e+00 604.3462
## Stage2:Z_star1 0.000000e+00 Inf 604.3462
## Stage3:Z_star1 0.000000e+00 Inf 604.3462
## Stage4:Z_star1 0.000000e+00 Inf 604.3462
## Age2 9.996170e-01 1.000773e+00 600.6815
## AscitesY2 4.474544e+00 4.478817e+06 600.6815
## SpidersY2 7.990245e-02 1.029105e+01 600.6815
## Alk_Phos2 9.999243e-01 1.000634e+00 600.6815
## SGOT2 9.920142e-01 1.020905e+00 600.6815
## Platelets2 9.968687e-01 1.020881e+00 600.6815
## Prothrombin2 1.940022e-01 4.689448e+00 600.6815
## Stage22 0.000000e+00 Inf 600.6815
## Stage32 0.000000e+00 Inf 600.6815
## Stage42 0.000000e+00 Inf 600.6815
## Z_star2 NA NA 600.6815
## Age:Z_star2 9.999587e-01 1.000030e+00 600.6815
## AscitesY:Z_star2 4.175688e-01 9.564533e-01 600.6815
## SpidersY:Z_star2 8.946642e-01 1.211095e+00 600.6815
## Alk_Phos:Z_star2 9.999632e-01 1.000008e+00 600.6815
## SGOT:Z_star2 9.990271e-01 1.000898e+00 600.6815
## Platelets:Z_star2 9.987788e-01 1.000257e+00 600.6815
## Prothrombin:Z_star2 9.303129e-01 1.129217e+00 600.6815
## Stage2:Z_star2 0.000000e+00 Inf 600.6815
## Stage3:Z_star2 0.000000e+00 Inf 600.6815
## Stage4:Z_star2 0.000000e+00 Inf 600.6815
## AscitesY3 2.034604e+00 1.595833e+06 599.4843
## SpidersY3 6.574306e-02 7.206378e+00 599.4843
## Alk_Phos3 9.999881e-01 1.000675e+00 599.4843
## SGOT3 9.919247e-01 1.021014e+00 599.4843
## Platelets3 9.964875e-01 1.019542e+00 599.4843
## Prothrombin3 2.067162e-01 4.864526e+00 599.4843
## Stage23 0.000000e+00 Inf 599.4843
## Stage33 0.000000e+00 Inf 599.4843
## Stage43 0.000000e+00 Inf 599.4843
## Z_star3 NA NA 599.4843
## AscitesY:Z_star3 4.435731e-01 1.001784e+00 599.4843
## SpidersY:Z_star3 9.158851e-01 1.227610e+00 599.4843
## Alk_Phos:Z_star3 9.999609e-01 1.000005e+00 599.4843
## SGOT:Z_star3 9.989815e-01 1.000857e+00 599.4843
## Platelets:Z_star3 9.988298e-01 1.000246e+00 599.4843
## Prothrombin:Z_star3 9.292650e-01 1.125843e+00 599.4843
## Stage2:Z_star3 0.000000e+00 Inf 599.4843
## Stage3:Z_star3 0.000000e+00 Inf 599.4843
## Stage4:Z_star3 0.000000e+00 Inf 599.4843
## AscitesY4 2.919844e+00 2.574537e+06 600.3745
## SpidersY4 1.611331e-01 1.371061e+01 600.3745
## SGOT4 9.907470e-01 1.020013e+00 600.3745
## Platelets4 9.989312e-01 1.022157e+00 600.3745
## Prothrombin4 2.145129e-01 4.316893e+00 600.3745
## Stage24 0.000000e+00 Inf 600.3745
## Stage34 0.000000e+00 Inf 600.3745
## Stage44 0.000000e+00 Inf 600.3745
## Z_star4 NA NA 600.3745
## AscitesY:Z_star4 4.326103e-01 9.833910e-01 600.3745
## SpidersY:Z_star4 8.794867e-01 1.161814e+00 600.3745
## SGOT:Z_star4 9.990440e-01 1.000928e+00 600.3745
## Platelets:Z_star4 9.986913e-01 1.000112e+00 600.3745
## Prothrombin:Z_star4 9.362371e-01 1.124439e+00 600.3745
## Stage2:Z_star4 2.335940e-271 2.240728e+269 600.3745
## Stage3:Z_star4 2.191938e-271 2.102544e+269 600.3745
## Stage4:Z_star4 2.169670e-271 2.081275e+269 600.3745
## AscitesY5 1.565589e+00 1.257239e+06 599.7154
## SpidersY5 1.597501e-01 1.208257e+01 599.7154
## SGOT5 9.908269e-01 1.019183e+00 599.7154
## Prothrombin5 2.113318e-01 4.472209e+00 599.7154
## Stage25 0.000000e+00 Inf 599.7154
## Stage35 0.000000e+00 Inf 599.7154
## Stage45 0.000000e+00 Inf 599.7154
## Z_star5 NA NA 599.7154
## AscitesY:Z_star5 4.514474e-01 1.020150e+00 599.7154
## SpidersY:Z_star5 8.855563e-01 1.162063e+00 599.7154
## SGOT:Z_star5 9.990964e-01 1.000923e+00 599.7154
## Prothrombin:Z_star5 9.344774e-01 1.125416e+00 599.7154
## Stage2:Z_star5 6.814091e-278 6.238793e+275 599.7154
## Stage3:Z_star5 6.602677e-278 6.045150e+275 599.7154
## Stage4:Z_star5 6.481425e-278 5.934325e+275 599.7154
## AscitesY6 1.903414e+00 1.561792e+06 591.7295
## SpidersY6 2.670145e-01 1.682927e+01 591.7295
## SGOT6 9.941431e-01 1.021841e+00 591.7295
## Prothrombin6 2.170679e-01 3.587086e+00 591.7295
## Z_star6 NA NA 591.7295
## AscitesY:Z_star6 4.471075e-01 1.010440e+00 591.7295
## SpidersY:Z_star6 8.726074e-01 1.132013e+00 591.7295
## SGOT:Z_star6 9.989113e-01 1.000679e+00 591.7295
## Prothrombin:Z_star6 9.486032e-01 1.125535e+00 591.7295
## AscitesY7 1.909030e+00 1.552808e+06 589.7380
## SpidersY7 1.249226e+00 2.971186e+00 589.7380
## SGOT7 9.941477e-01 1.021776e+00 589.7380
## Prothrombin7 2.242602e-01 3.549938e+00 589.7380
## Z_star7 NA NA 589.7380
## AscitesY:Z_star7 4.472534e-01 1.010228e+00 589.7380
## SGOT:Z_star7 9.989143e-01 1.000679e+00 589.7380
## Prothrombin:Z_star7 9.491754e-01 1.123362e+00 589.7380
## AscitesY8 1.765144e+00 1.451758e+06 587.9379
## SpidersY8 1.244200e+00 2.955521e+00 587.9379
## SGOT8 1.001574e+00 1.007944e+00 587.9379
## Prothrombin8 2.234652e-01 3.369459e+00 587.9379
## Z_star8 NA NA 587.9379
## AscitesY:Z_star8 4.490406e-01 1.014866e+00 587.9379
## Prothrombin:Z_star8 9.527223e-01 1.123997e+00 587.9379
## AscitesY9 1.965532e+00 1.323917e+06 586.6079
## SpidersY9 1.226319e+00 2.904306e+00 586.6079
## SGOT9 1.001501e+00 1.007856e+00 586.6079
## Prothrombin9 1.182620e+00 1.921611e+00 586.6079
## Z_star9 NA NA 586.6079
## AscitesY:Z_star9 4.528318e-01 1.012068e+00 586.6079
## SpidersY10 1.226319e+00 2.904306e+00 586.6079
## SGOT10 1.001501e+00 1.007856e+00 586.6079
## Prothrombin10 1.182620e+00 1.921611e+00 586.6079
## AscitesY10 1.965532e+00 1.323917e+06 586.6079
## Z_star10 NA NA 586.6079
## AscitesY:Z_star10 4.528318e-01 1.012068e+00 586.6079write.csv(stra.int_summary, "stratifiedmodel_int.csv", row.names = FALSE)Model Strata Int Final : strata.int9
strata.int_final = strata.int9
summary(strata.int_final)## Call:
## coxph(formula = y ~ Ascites + Spiders + SGOT + Prothrombin +
## Ascites * Z_star + strata(Z_star), data = df)
##
## n= 276, number of events= 111
##
## coef exp(coef) se(coef) z Pr(>|z|)
## AscitesY 7.386e+00 1.613e+03 3.424e+00 2.157 0.030979 *
## SpidersY 6.351e-01 1.887e+00 2.199e-01 2.888 0.003883 **
## SGOT 4.663e-03 1.005e+00 1.614e-03 2.890 0.003856 **
## Prothrombin 4.104e-01 1.507e+00 1.238e-01 3.314 0.000918 ***
## Z_star NA NA 0.000e+00 NA NA
## AscitesY:Z_star -3.901e-01 6.770e-01 2.052e-01 -1.901 0.057237 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## AscitesY 1613.134 0.0006199 1.9655 1.324e+06
## SpidersY 1.887 0.5298794 1.2263 2.904e+00
## SGOT 1.005 0.9953479 1.0015 1.008e+00
## Prothrombin 1.507 0.6633530 1.1826 1.922e+00
## Z_star NA NA NA NA
## AscitesY:Z_star 0.677 1.4771574 0.4528 1.012e+00
##
## Concordance= 0.704 (se = 0.034 )
## Likelihood ratio test= 42.7 on 5 df, p=4e-08
## Wald test = 39.29 on 5 df, p=2e-07
## Score (logrank) test = 44.42 on 5 df, p=2e-08Asumsi PH Model Strata Int Final
ph.test_stra.int = cox.zph(strata.int_final)
print(ph.test_stra.int)## chisq df p
## Ascites 0.5484 1 0.459
## Spiders 0.0372 1 0.847
## SGOT 0.2074 1 0.649
## Prothrombin 2.8117 1 0.094
## Ascites:Z_star 0.6782 1 0.410
## GLOBAL 5.1567 5 0.397Perbandingan : Mana model stratified terbaik antara Interaksi vs Non-Interaksi?
LL_noint = strata_model_final$loglik[2]
LL_int = strata.int_final$loglik[2]
chisq_stat = -2 * (LL_noint - LL_int)
ncoef_noint = length(strata_model_final$coefficients)
ncoef_int = length(strata.int_final$coefficients)
ncoef_diff = ncoef_int - ncoef_noint
p_value = 1 - pchisq(chisq_stat, df = ncoef_diff)
chisq_table = qchisq(0.95, df = ncoef_diff)
decision = ifelse(p_value < 0.05, "Tolak H0", "Gagal Tolak H0")
conclusion = ifelse(p_value < 0.05,
"Ada bukti cukup untuk menyatakan bahwa interaksi meningkatkan model.",
"Tidak ada bukti cukup untuk menyatakan bahwa interaksi meningkatkan model.")
loglik_comparison = data.frame(
Keterangan = c(
"Loglik Model Tanpa Interaksi",
"Loglik Model Dengan Interaksi",
"DF",
"Chi-Square Hitung",
"Chi-Square Tabel",
"P-Value",
"Keputusan",
"Kesimpulan"
),
Nilai = c(
LL_noint,
LL_int,
ncoef_diff,
chisq_stat,
chisq_table,
p_value,
decision,
conclusion
)
)
# Print data frame
print(loglik_comparison)## Keterangan
## 1 Loglik Model Tanpa Interaksi
## 2 Loglik Model Dengan Interaksi
## 3 DF
## 4 Chi-Square Hitung
## 5 Chi-Square Tabel
## 6 P-Value
## 7 Keputusan
## 8 Kesimpulan
## Nilai
## 1 -290.6135953816
## 2 -288.303971454406
## 3 2
## 4 4.61924785438771
## 5 5.99146454710798
## 6 0.0992985880372381
## 7 Gagal Tolak H0
## 8 Tidak ada bukti cukup untuk menyatakan bahwa interaksi meningkatkan model.