# Dr Nichanan
# Nichanan Jongthitinon <soundnichanan _at_ gmail.com>
library(epicalc, warn.conflicts = FALSE)
## Loading required package: foreign
## Loading required package: survival
## Loading required package: MASS
## Loading required package: nnet
library(readxl, warn.conflicts = FALSE)
library(dplyr, warn.conflicts = FALSE)
library(tidyr, warn.conflicts = FALSE)
library(broom, warn.conflicts = FALSE)
library(gtsummary, warn.conflicts = FALSE)
library(flextable, warn.conflicts = FALSE)
library(survival, warn.conflicts = FALSE)
library(survminer, warn.conflicts = FALSE)
## Loading required package: ggplot2
##
## Attaching package: 'ggplot2'
## The following object is masked from 'package:epicalc':
##
## alpha
## Loading required package: ggpubr
##
## Attaching package: 'ggpubr'
## The following objects are masked from 'package:flextable':
##
## border, font, rotate
options("width"=300)
setwd("D:/Consults/Thirachit/Nichanan/HLK")
source("D:/Consults/Thirachit/leukemia/TableStack.R")
data1 <- read_excel("Final data1.xlsx")
## Warning: Expecting numeric in BT57 / R57C72: got a date
## Warning: Expecting numeric in BT96 / R96C72: got a date
## Warning: Expecting numeric in BT120 / R120C72: got a date
## Warning: Expecting numeric in BT210 / R210C72: got a date
## Warning: Expecting numeric in BT243 / R243C72: got a date
## Warning: Expecting numeric in BT249 / R249C72: got a date
## Warning: Expecting numeric in BT251 / R251C72: got a date
## Warning: Expecting numeric in BT253 / R253C72: got a date
## Warning: Expecting numeric in BT291 / R291C72: got a date
## Warning: Expecting numeric in BT305 / R305C72: got a date
## Warning: Expecting numeric in BT363 / R363C72: got a date
## Warning: Expecting numeric in BT410 / R410C72: got a date
## Warning: Expecting numeric in BT419 / R419C72: got a date
## Warning: Expecting numeric in BT440 / R440C72: got a date
## Warning: Expecting numeric in BT441 / R441C72: got a date
## Warning: Expecting numeric in BT498 / R498C72: got a date
## Warning: Expecting numeric in BT583 / R583C72: got a date
## Warning: Expecting numeric in BT590 / R590C72: got a date
## Warning: Expecting numeric in BT658 / R658C72: got a date
## Warning: Expecting numeric in BT719 / R719C72: got a date
## Warning: Expecting numeric in BT794 / R794C72: got a date
## Warning: Expecting numeric in BT841 / R841C72: got a date
## Warning: Expecting numeric in BT970 / R970C72: got a date
## Warning: Expecting numeric in BT987 / R987C72: got a date
## Warning: Expecting numeric in BT1104 / R1104C72: got a date
## New names:
## • `if yes (protocol)` -> `if yes (protocol)...56`
## • `start date` -> `start date...57`
## • `if yes (date)` -> `if yes (date)...59`
## • `if yes (date)` -> `if yes (date)...61`
## • `if yes (date)` -> `if yes (date)...63`
## • `site` -> `site...64`
## • `if combined` -> `if combined...65`
## • `if yes (protocol)` -> `if yes (protocol)...67`
## • `start date` -> `start date...68`
## • `if yes (date)` -> `if yes (date)...70`
## • `if yes (date)` -> `if yes (date)...72`
## • `if yes (date)` -> `if yes (date)...74`
## • `site` -> `site...75`
## • `if combined` -> `if combined...76`
## • `` -> `...81`
data2 <- read_excel("Final data2.xlsx")
## New names:
## • `Others` -> `Others...8`
## • `68. Neurological complication` -> `68. Neurological complication...63`
## • `Others` -> `Others...80`
## • `68. Neurological complication` -> `68. Neurological complication...108`
data1
## # A tibble: 1,133 × 81
## `1.site` Year_diag No_year 2.regi…¹ HN Sex place dob `A_ visit` A_dx Age BW HT Right Right…² Mor 13.1A…³ 13.2A…⁴ Chro Chro_…⁵ 15.1s…⁶ wbc hb hct plt blast 16.1s…⁷ bun creat…⁸ calcium phosp…⁹ uric SGOT SGPT ALP LDH Media…˟
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dttm> <dttm> <dttm> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 PSU 2521 1 NA 28952 1 1 2519-11-12 00:00:00 2521-10-16 00:00:00 2521-10-16 00:00:00 23 9 NA 4 social… 2 NA 8 1 <NA> NA 20200 12.6 35 120000 2 NA NA NA NA NA NA NA NA NA NA 2
## 2 PSU 2522 1 NA 5045 2 1 2521-03-11 00:00:00 2522-02-09 00:00:00 2522-02-09 00:00:00 11 8 NA 4 social… 2 NA 8 1 <NA> NA 95000 6.8 18.5 NA 85 NA NA NA NA NA NA NA NA NA NA 2
## 3 PSU 2522 2 NA 9559 2 7 2520-03-09 00:00:00 2522-03-09 00:00:00 2522-03-09 00:00:00 24 10 NA 4 social… 2 NA 8 1 <NA> NA 19900 2.6 8 120000 68 NA NA NA NA NA NA NA NA NA NA 2
## 4 PSU 2522 3 NA 28109 2 1 2521-06-21 00:00:00 2522-06-19 00:00:00 2522-06-20 00:00:00 12 8 82 4 social… 1 3 NA 1 <NA> NA 1900 6.2 22 120000 2 NA 10 NA NA NA NA NA NA NA NA 2
## 5 PSU 2522 4 NA 11457 1 1 2520-05-16 00:00:00 2522-11-16 00:00:00 2522-11-27 00:00:00 30 11.5 83 4 social… 1 3 NA 1 <NA> NA 2000 4.1 11.5 70000 10 NA NA NA NA NA NA NA NA NA NA 2
## 6 PSU 2523 1 NA 15875 2 1 2521-03-01 00:00:00 2523-03-07 00:00:00 2523-03-07 00:00:00 24 10 NA 4 social… 1 3 NA 1 <NA> NA 6400 8.2 17 30000 98 NA NA NA NA NA NA NA NA NA NA 2
## 7 PSU 2523 2 NA 30558 2 1 2522-03-20 00:00:00 2523-06-16 00:00:00 2523-06-18 00:00:00 15 8 77.5 4 social… 1 3 NA 1 <NA> NA 14150 7.6 24 120000 15 NA 9 6.8 NA NA 5.25 NA NA NA NA 2
## 8 PSU 2523 3 NA 33787 1 7 2522-07-15 00:00:00 2523-06-15 00:00:00 2523-06-15 00:00:00 11 10 NA 4 social… 2 NA 8 1 <NA> NA 265000 8 20 88000 65 NA NA NA NA NA NA NA NA NA NA 2
## 9 PSU 2523 4 NA 33959 2 2 2520-11-04 00:00:00 2523-06-20 00:00:00 2523-06-24 00:00:00 31 8 80 4 social… 1 3 NA 1 <NA> NA 122000 5.6 12 75000 93 NA NA NA NA NA NA NA NA NA NA 2
## 10 PSU 2525 2 NA 2350 1 2 2517-12-01 00:00:00 2525-02-01 00:00:00 2525-02-01 00:00:00 86 15 106. 4 social… 2 NA 8 1 <NA> NA 19500 8.4 28 120000 80 NA NA NA NA NA NA NA NA NA NA 2
## # … with 1,123 more rows, 44 more variables: Terticular_mass <dbl>, Fever <dbl>, Arthitis <dbl>, Arthitis_d <chr>, Xray <dbl>, hepato <dbl>, hepato_size <dbl>, Splenomegaly <dbl>, Spleen_size <dbl>, lymphodenopathy <dbl>, Exopthalmos <dbl>, Skin_bleeding <dbl>, Gum_hypertrophy <dbl>,
## # Bleed_gum <dbl>, Jaundice <dbl>, Underlying <dbl>, Underlying_d <chr>, `31.1st protocol chemo.` <dbl>, `if yes (protocol)...56` <chr>, `start date...57` <dttm>, `31.1 remission` <dbl>, `if yes (date)...59` <dttm>, `31.2 complete protocol` <dbl>, `if yes (date)...61` <dttm>,
## # `31.3 relapsed` <dbl>, `if yes (date)...63` <dttm>, site...64 <dbl>, `if combined...65` <chr>, `32. 2nd protocol chemo.` <dbl>, `if yes (protocol)...67` <chr>, `start date...68` <dttm>, `32.1 remission` <dbl>, `if yes (date)...70` <dttm>, `32.2 complete protocol` <dbl>,
## # `if yes (date)...72` <dbl>, `32.3 relapsed` <dbl>, `if yes (date)...74` <dttm>, site...75 <dbl>, `if combined...76` <chr>, `33. status` <dbl>, `if death, cause` <dbl>, `date of death` <dttm>, `34. Last FU` <dttm>, ...81 <chr>, and abbreviated variable names ¹`2.register no`, ²Right_other,
## # ³`13.1ALL`, ⁴`13.2ANLL`, ⁵Chro_Others, ⁶`15.1st CBC`, ⁷`16.1st Serum chem`, ⁸creatinine, ⁹phosphorus, ˟Mediastinal_mass
## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
data2
## # A tibble: 740 × 117
## start end 1. Site nu…¹ HN Year_…² No_year Dx Other…³ Sex A_CMT A_pro…⁴ A_date A_Rem…⁵ A_Remiss_day A_Comp A_LastCMT A_Rel…⁶ A_Relapse_date A_Sit…⁷ A_BM A_CNS A_tes…⁸ A_ Co…⁹ A_com…˟ A_typ…˟ B_CMT B_pro…˟ B_date
## <dttm> <dttm> <lgl> <chr> <dbl> <dbl> <chr> <lgl> <chr> <chr> <chr> <dttm> <chr> <dttm> <chr> <dttm> <chr> <dttm> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr> <dttm>
## 1 2021-09-27 19:04:34 2021-11-06 20:24:48 NA 2790… 2542 4 76.1… NA 1. M… 1. Y… MCCG 1… 1999-03-24 00:00:00 1. Yes 1999-05-07 00:00:00 1. Yes 2002-05-13 00:00:00 2. No NA <NA> NA NA NA NA <NA> <NA> 2. No <NA> NA
## 2 2021-09-27 19:07:11 2021-09-27 19:09:17 NA 8908… 2542 5 76.1… NA 1. M… 1. Y… MCCG 1… 1999-04-01 00:00:00 1. Yes 1999-05-10 00:00:00 1. Yes 2002-05-20 00:00:00 2. No NA <NA> NA NA NA NA <NA> <NA> 2. No <NA> NA
## 3 2021-09-26 00:12:29 2021-09-26 07:33:09 NA 8375… 2541 11 76.1… NA 1. M… 1. Y… MCCG 1… 1998-03-13 00:00:00 1. Yes 1998-04-21 00:00:00 1. Yes 2001-04-02 00:00:00 2. No NA <NA> NA NA NA NA <NA> <NA> 2. No <NA> NA
## 4 2021-09-26 07:33:09 2021-09-26 07:36:22 NA 8368… 2541 12 76.1… NA 2. F… 1. Y… MCCG 1… 1998-04-23 00:00:00 3. Not… NA 2. No NA 2. No NA <NA> NA NA NA NA <NA> <NA> <NA> <NA> NA
## 5 2021-09-27 19:09:17 2021-09-27 19:11:44 NA 8931… 2542 6 76.1… NA 2. F… 1. Y… MCCG 1… 1999-04-16 00:00:00 3. Not… NA 2. No NA 2. No NA <NA> NA NA NA NA <NA> <NA> 2. No <NA> NA
## 6 2021-09-26 07:36:23 2021-09-26 07:42:54 NA 8378… 2541 13 76.1… NA 1. M… 1. Y… MCCG 1… 1998-04-24 00:00:00 1. Yes 1998-06-05 00:00:00 2. No NA 2. No NA <NA> NA NA NA NA <NA> <NA> 2. No <NA> NA
## 7 2021-09-26 07:42:54 2021-09-26 07:45:28 NA 8354… 2541 14 76.1… NA 1. M… 1. Y… MCCG 1… 1998-05-02 00:00:00 1. Yes 1998-06-08 00:00:00 1. Yes 2001-06-11 00:00:00 2. No NA <NA> NA NA NA NA <NA> <NA> 2. No <NA> NA
## 8 2021-09-26 07:51:11 2021-09-26 07:56:15 NA 8393… 2541 16 76.1… NA 2. F… 1. Y… MCCG 1… 1998-05-09 00:00:00 1. Yes 1998-06-17 00:00:00 1. Yes 2001-06-24 00:00:00 1. Yes 2003-09-18 00:00:00 1. BM 1 0 0 0 <NA> 4. N/A 1. Y… Refrac… 2003-09-18 00:00:00
## 9 2021-09-26 07:56:15 2021-09-26 08:01:31 NA 8428… 2541 17 76.2… NA 1. M… 1. Y… BFM 83 1998-05-27 00:00:00 2. No NA 2. No NA 2. No NA <NA> NA NA NA NA <NA> <NA> 1. Y… BFM 83 1998-06-23 00:00:00
## 10 2021-09-26 08:01:31 2021-09-26 08:04:56 NA 8457… 2541 18 76.2… NA 1. M… 1. Y… BFM 83 1998-06-17 00:00:00 1. Yes 1998-06-29 00:00:00 1. Yes 2000-12-07 00:00:00 2. No NA <NA> NA NA NA NA <NA> <NA> 2. No <NA> NA
## # … with 730 more rows, 89 more variables: B_Remiss <chr>, B_remiss_day <dttm>, B_Comp <chr>, B_LastCMT <dttm>, B_relapsed <chr>, B_relapsed_date <dttm>, B_Site_relapsed <chr>, B_BM <dbl>, B_CNS <dbl>, B_testis <dbl>, `B_ Combine` <dbl>, B_type_relapsed <chr>, C_CMT <chr>, C_protocol <chr>,
## # C_date <dttm>, C_Remiss <chr>, C_remiss_day <dttm>, C_Comp <chr>, C_LastCMT <dttm>, C_relapsed <chr>, C_relapsed_date <dttm>, C_Site_relapsed <chr>, C_BM <dbl>, C_CNS <dbl>, C_testis <dbl>, `C_ Combine` <dbl>, C_type_relapsed <chr>, Status <chr>, death_cause <chr>, death_date <dttm>,
## # Last_date <dttm>, CNS_diagnosis <chr>, `67. Metabolic complication` <lgl>, TLS <chr>, `68. Neurological complication...63` <lgl>, Seizure <chr>, ICH <chr>, Ischemic_stroke <chr>, IICP <chr>, Imaging <chr>, Surgery <chr>, Finding <chr>, Operation <chr>, `69. Respiratory complication` <lgl>,
## # `Respiratory _status` <chr>, `69.2 Diagnosis` <lgl>, Pneumonia <chr>, Pleural_effusion <chr>, ARDS <chr>, Pul_leuko <chr>, `Pul_ hem` <chr>, Others...80 <chr>, Others_data <chr>, DIC <chr>, `71. Renal complication` <lgl>, Renal_insuf <chr>, RRT <chr>, RRT_med <chr>, `72. Infection` <lgl>,
## # Infect <chr>, Sepsis <chr>, `Sepsis_ site` <chr>, `site_ CNS` <dbl>, Site_respi <dbl>, Site_CVS <dbl>, Site_GI <dbl>, Site_GU <dbl>, Site_skin <dbl>, Site_others <dbl>, `site_ CNS_d` <lgl>, Site_respi_d <chr>, Site_CVS_d <lgl>, Site_GI_d <chr>, Site_GU_d <chr>, Site_skin_d <chr>,
## # `Site_others _d` <chr>, `73. Management` <lgl>, Intubation <chr>, ICU <chr>, `68. Neurological complication...108` <lgl>, `_id` <dbl>, `_uuid` <chr>, `_submission_time` <dttm>, `_validation_status` <lgl>, `_notes` <lgl>, `_status` <chr>, `_submitted_by` <lgl>, `_tags` <lgl>, `_index` <dbl>, and
## # abbreviated variable names ¹`1. Site number`, ²Year_diag, ³Others...8, ⁴A_protocol, ⁵A_Remiss, ⁶A_Relapsed, ⁷A_Site_relapsed, ⁸A_testis, ⁹`A_ Combine`, ˟A_combine_data, ˟A_type_relapsed, ˟B_protocol
## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
names(data1)[17] <- "ALL"
names(data1)[55] <- "chemotherapy"
# Omit 1 cases with 'HN' 1302964 and 'No_year' 4 (duplicate see email of Sep 25, 2022)
data1 <- filter(data1, !(HN==1302964 & No_year==4))
# Omit variable 'Sex' from data2 (already exists in data1)
data2$Sex <- NULL
any(is.na(data1$HN))
## [1] FALSE
data1$HN <- as.character(data1$HN)
# Merge
data <- inner_join(data1, data2, by=c("HN","Year_diag","No_year"))
dim(data)
## [1] 740 194
length(unique(data$HN))
## [1] 740
group_by(data, HN) %>%
filter(n()>1) %>%
select(1:11)
## # A tibble: 0 × 11
## # Groups: HN [0]
## # … with 11 variables: 1.site <chr>, Year_diag <dbl>, No_year <dbl>, 2.register no <dbl>, HN <chr>, Sex <dbl>, place <dbl>, dob <dttm>, A_ visit <dttm>, A_dx <dttm>, Age <dbl>
## # ℹ Use `colnames()` to see all variable names
group_by(data, HN) %>%
filter(n()>1) %>%
select(12:29)
## Adding missing grouping variables: `HN`
## # A tibble: 0 × 19
## # Groups: HN [0]
## # … with 19 variables: HN <chr>, BW <dbl>, HT <dbl>, Right <dbl>, Right_other <chr>, Mor <dbl>, ALL <dbl>, 13.2ANLL <dbl>, Chro <dbl>, Chro_Others <chr>, 15.1st CBC <lgl>, wbc <dbl>, hb <dbl>, hct <dbl>, plt <dbl>, blast <dbl>, 16.1st Serum chem <lgl>, bun <dbl>, creatinine <dbl>
## # ℹ Use `colnames()` to see all variable names
# Keep if received chemotherapy
count(data, chemotherapy)
## # A tibble: 2 × 2
## chemotherapy n
## <dbl> <int>
## 1 1 690
## 2 2 50
data <- filter(data, chemotherapy==1)
#
# Corrections:
#
# HN 839991 died on July 28, 2549. (2006)
data[, c('Status', 'death_date', 'Last_date')][data$HN=="839991",]
## # A tibble: 0 × 3
## # … with 3 variables: Status <chr>, death_date <dttm>, Last_date <dttm>
## # ℹ Use `colnames()` to see all variable names
data$death_date[data$HN=="839991"] <- as.Date("2006-09-28")
# HN 842825 died on February 14, 2545. (2002)
data[, c('Status', 'death_date', 'Last_date')][data$HN=="842825",]
## # A tibble: 1 × 3
## Status death_date Last_date
## <chr> <dttm> <dttm>
## 1 4. Death 2021-02-14 00:00:00 NA
data$death_date[data$HN=="842825"] <- as.Date("2002-02-14")
# HN 865930 lost follow-up, and we lack the information of relapse. and patient died on July 29, 2542. (1999)
data[, c('A_Relapsed', 'Status', 'death_cause', 'death_date', 'Last_date')][data$HN=="865930",]
## # A tibble: 1 × 5
## A_Relapsed Status death_cause death_date Last_date
## <chr> <chr> <chr> <dttm> <dttm>
## 1 2. No 4. Death 1. Cancer related 2021-07-27 00:00:00 NA
# Assume relapse occurred at death
data$A_Relapsed[data$HN=="865930"] <- "1. Yes"
data$A_Relapse_date[data$HN=="865930"] <- as.Date("1999-09-29")
count(data, A_Relapsed)
## # A tibble: 3 × 2
## A_Relapsed n
## <chr> <int>
## 1 1. Yes 225
## 2 2. No 464
## 3 <NA> 1
filter(data, is.na(A_Relapsed)) %>% select(1:9, `31.1 remission`, `31.3 relapsed`, Status, `if death, cause`)
## # A tibble: 1 × 13
## `1.site` Year_diag No_year `2.register no` HN Sex place dob `A_ visit` `31.1 remission` `31.3 relapsed` Status `if death, cause`
## <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dttm> <dttm> <dbl> <dbl> <chr> <dbl>
## 1 PSU 2554 25 1809902669426 1724729 2 8 2554-03-17 00:00:00 2554-08-01 00:00:00 2 2 4. Death 1
# Assume this cases did not relapse
data$A_Relapsed[data$HN=='1924929'] <- "2. No"
#
# Analysis
#
# wbc count at initial diagnosis(>/= 100000(hyperleukocytosis) and < 100000 comparison of 2 groups in term of basic patient characteristics, clinical characteristics and complication during first induction of the patients, overall survival and event free survival at 5 years and 10 years after diagnosis between hyperleukocytosis and non-hyperleukocytosis group.
# Data management
YN <- c('Yes', 'No')
count(data, ALL)
## # A tibble: 4 × 2
## ALL n
## <dbl> <int>
## 1 1 63
## 2 2 376
## 3 3 44
## 4 NA 207
count(data, Mor, ALL)
## # A tibble: 4 × 3
## Mor ALL n
## <dbl> <dbl> <int>
## 1 1 1 63
## 2 1 2 376
## 3 1 3 44
## 4 2 NA 207
# Convert categorical data to factors
data <- mutate_if(data, is.character, as.factor)
# The early complication variables have NA (TLS also has some with "3. N/A")
data <- mutate(data,
hlk=factor(ifelse(wbc<100000, 1, 2), labels=c('No','Yes')),
agegp=cut(Age, breaks=c(0,24,48,92,Inf), labels=c('0-23','24-49','48-91','92+'), right=FALSE, include=TRUE),
sex=case_when(Sex==1~'Male',
Sex==2~'Female',
TRUE~NA_character_),
A_dx=epicalc::be2ad(as.Date(A_dx)),
Mor=factor(Mor, labels=c('ALL', 'AML')),
ALL=factor(ALL, labels=c('T_cell','B-cell','FAB')),
Mediastinal_mass=factor(Mediastinal_mass, levels=c(1,2), labels=YN),
Terticular_mass=factor(Terticular_mass, levels=c(1,2), labels=YN),
Fever=factor(Fever, levels=c(1,2), labels=YN),
Arthitis=factor(Arthitis, levels=c(1,2), labels=YN),
Xray=factor(Xray, levels=c(1,2), labels=YN),
hepato=factor(hepato, levels=c(1,2), labels=YN),
Splenomegaly=factor(Splenomegaly, levels=c(1,2), labels=YN),
lymphodenopathy=factor(lymphodenopathy, levels=c(1,2), labels=YN),
Exopthalmos=factor(Exopthalmos, levels=c(1,2), labels=YN),
Skin_bleeding=factor(Skin_bleeding, levels=c(1,2), labels=YN),
Gum_hypertrophy=factor(Gum_hypertrophy, levels=c(1,2), labels=YN),
Bleed_gum=factor(Bleed_gum, levels=c(1,2), labels=YN),
Jaundice=factor(Jaundice, levels=c(1,2), labels=YN),
Underlying=factor(Underlying, levels=c(1,2), labels=YN),
A_type_relapsed=na_if(A_type_relapsed, "4. N/A"),
rel_BM=factor(ifelse(grepl('BM|Bone marrow', A_Site_relapsed), 1, 2), labels=c("Yes","No")),
rel_CNS=factor(ifelse(grepl('CNS', A_Site_relapsed), 1, 2), labels=c("Yes","No")),
rel_Testis=factor(ifelse(grepl('Testis', A_Site_relapsed), 1, 2), labels=c("Yes","No")),
rdate=as.Date(coalesce(A_Relapse_date, B_relapsed_date, C_relapsed_date)),
outcome=factor(Status=="4. Death", labels=c("Alive","Dead")),
death_date=as.Date(death_date),
Last_date=as.Date(Last_date),
sdate=as.Date(case_when(outcome=="Dead"~death_date,
TRUE~Last_date)),
stime=as.numeric(difftime(sdate, A_dx, units="days")/365.25),
event=outcome=="Dead" | !is.na(rdate),
edate=if_else(is.na(rdate), sdate, rdate),
etime=as.numeric(difftime(edate, A_dx, units="days")/365.25),
TLS=factor(na_if(TLS, "3. N/A")))
count(data, hlk)
## # A tibble: 2 × 2
## hlk n
## <fct> <int>
## 1 No 568
## 2 Yes 122
summary(data)
## 1.site Year_diag No_year 2.register no HN Sex place dob A_ visit A_dx Age BW HT Right Right_other Mor
## PSU:690 Min. :2541 Min. : 1.00 Min. :9.402e+10 1001165: 1 Min. :1.000 Min. : 1.000 Min. :2526-09-19 00:00:00.0 Min. :2541-01-08 00:00:00.0 Min. :1998-01-09 Min. : 2.00 Min. : 2.10 Min. : 44.0 Min. :1.000 social payment: 1 ALL:483
## 1st Qu.:2546 1st Qu.:10.00 1st Qu.:1.811e+12 1002370: 1 1st Qu.:1.000 1st Qu.: 2.000 1st Qu.:2539-06-27 12:00:00.0 1st Qu.:2546-02-13 12:00:00.0 1st Qu.:2003-02-15 1st Qu.: 34.00 1st Qu.: 12.20 1st Qu.: 91.0 1st Qu.:1.000 Social payment: 1 AML:207
## Median :2550 Median :19.50 Median :1.910e+12 1003096: 1 Median :1.000 Median : 6.000 Median :2544-11-09 12:00:00.0 Median :2550-05-15 00:00:00.0 Median :2007-05-16 Median : 55.50 Median : 16.00 Median :105.0 Median :1.000 NA's :688
## Mean :2550 Mean :21.77 Mean :1.907e+12 1007357: 1 Mean :1.425 Mean : 5.499 Mean :2544-07-24 12:35:28.7 Mean :2550-07-10 19:09:54.7 Mean :2007-07-14 Mean : 71.67 Mean : 20.64 Mean :110.1 Mean :1.104
## 3rd Qu.:2554 3rd Qu.:31.00 3rd Qu.:1.949e+12 1007449: 1 3rd Qu.:2.000 3rd Qu.: 8.000 3rd Qu.:2549-05-22 06:00:00.0 3rd Qu.:2554-01-29 18:00:00.0 3rd Qu.:2011-02-07 3rd Qu.:105.00 3rd Qu.: 24.45 3rd Qu.:127.9 3rd Qu.:1.000
## Max. :2560 Max. :71.00 Max. :5.961e+12 1009398: 1 Max. :2.000 Max. :15.000 Max. :2559-03-13 00:00:00.0 Max. :2560-12-26 00:00:00.0 Max. :2017-12-28 Max. :180.00 Max. :112.00 Max. :177.0 Max. :4.000
## NA's :71 (Other):684
## ALL 13.2ANLL Chro Chro_Others 15.1st CBC wbc hb hct plt blast 16.1st Serum chem bun creatinine calcium phosphorus
## T_cell: 63 Min. :1.00 Min. :1.000 47, xy, 21 : trisomy 21 male : 2 Mode:logical Min. : 600 Min. : 1.400 Min. : 4.30 Min. : 2000 Min. : 0.00 Mode:logical Min. : 2.20 Min. :0.1300 Min. : 4.000 Min. : 0.310
## B-cell:376 1st Qu.:4.25 1st Qu.:2.000 Mosaic translocation (8;21) male karyotype : 2 NA's:690 1st Qu.: 6165 1st Qu.: 5.600 1st Qu.:17.02 1st Qu.: 19000 1st Qu.: 13.00 NA's:690 1st Qu.: 8.70 1st Qu.:0.3600 1st Qu.: 9.000 1st Qu.: 4.100
## FAB : 44 Median :5.00 Median :3.000 ?Translocation (8;14) female karyotype : 1 Median : 17525 Median : 7.300 Median :22.00 Median : 39500 Median : 49.00 Median : 11.30 Median :0.5000 Median : 9.400 Median : 4.800
## NA's :207 Mean :5.33 Mean :2.851 ?Translocation(1;7)and(21;21)male karyotype : 1 Mean : 66157 Mean : 7.423 Mean :22.46 Mean : 72060 Mean : 48.15 Mean : 12.22 Mean :0.5486 Mean : 9.476 Mean : 4.767
## 3rd Qu.:7.00 3rd Qu.:3.000 37-46,xx,mul:multiple anomalies Female karyotype,major: 1 3rd Qu.: 60600 3rd Qu.: 9.100 3rd Qu.:27.50 3rd Qu.: 86000 3rd Qu.: 85.00 3rd Qu.: 14.30 3rd Qu.:0.6600 3rd Qu.: 9.900 3rd Qu.: 5.500
## Max. :8.00 Max. :4.000 (Other) :136 Max. :1251700 Max. :64.000 Max. :43.00 Max. :823000 Max. :100.00 Max. :118.60 Max. :6.0000 Max. :18.400 Max. :19.300
## NA's :484 NA's :1 NA's :547 NA's :1
## uric SGOT SGPT ALP LDH Mediastinal_mass Terticular_mass Fever Arthitis Arthitis_d Xray hepato hepato_size Splenomegaly Spleen_size lymphodenopathy Exopthalmos Skin_bleeding Gum_hypertrophy Bleed_gum
## Min. : 1.100 Min. : 7.00 Min. : 2.00 Min. : 4.0 Min. : 11.0 Yes: 24 Yes: 0 Yes:517 Yes: 54 Rt.elbow : 4 Yes: 50 Yes:613 Min. : 0.000 Yes:451 Min. : 0.000 Yes:531 Yes: 28 Yes:283 Yes: 66 Yes:101
## 1st Qu.: 3.900 1st Qu.: 24.00 1st Qu.: 10.00 1st Qu.: 107.0 1st Qu.: 644.5 No :666 No :690 No :173 No :636 knee : 3 No :640 No : 77 1st Qu.: 3.000 No :239 1st Qu.: 0.000 No :159 No :662 No :407 No :624 No :589
## Median : 5.200 Median : 32.00 Median : 15.00 Median : 141.5 Median : 1063.5 Lt.knee : 2 Median : 4.000 Median : 3.000
## Mean : 5.806 Mean : 51.31 Mean : 30.99 Mean : 167.2 Mean : 2555.3 all joint: 1 Mean : 4.269 Mean : 3.411
## 3rd Qu.: 6.800 3rd Qu.: 49.00 3rd Qu.: 28.00 3rd Qu.: 193.0 3rd Qu.: 2301.2 ankle : 1 3rd Qu.: 6.000 3rd Qu.: 5.000
## Max. :43.900 Max. :1039.00 Max. :640.00 Max. :1055.0 Max. :46050.0 (Other) : 43 Max. :15.000 Max. :16.000
## NA's :6 NA's :636 NA's :9 NA's :55
## Jaundice Underlying Underlying_d chemotherapy if yes (protocol)...56 start date...57 31.1 remission if yes (date)...59 31.2 complete protocol if yes (date)...61 31.3 relapsed if yes (date)...63
## Yes: 6 Yes: 39 Down's syndrome : 24 Min. :1 TPOG ALL 01-05 :130 Min. :2541-01-14 00:00:00.0 Min. :1.000 Min. :2541-02-19 00:00:00.0 Min. :1.000 Min. :2543-06-22 00:00:00.0000 Min. :1.000 Min. :2542-06-14 00:00:00.0000
## No :684 No :651 SAO : 2 1st Qu.:1 Modified CCG 106:112 1st Qu.:2546-02-16 18:00:00.0 1st Qu.:1.000 1st Qu.:2546-05-20 00:00:00.0 1st Qu.:1.000 1st Qu.:2550-03-12 00:00:00.0000 1st Qu.:1.000 1st Qu.:2547-09-06 06:00:00.0000
## alpha thalassemia trait : 1 Median :1 ANLL-BFM 83 : 79 Median :2550-05-27 00:00:00.0 Median :1.000 Median :2550-07-24 00:00:00.0 Median :2.000 Median :2553-12-13 00:00:00.0000 Median :2.000 Median :2551-10-15 00:00:00.0000
## ASD -> SIP ASD closure 2551: 1 Mean :1 TPOG ALL 02-05 : 62 Mean :2550-07-26 07:39:07.7 Mean :1.348 Mean :2550-09-30 04:13:04.5 Mean :1.517 Mean :2553-10-23 21:58:55.0999 Mean :1.672 Mean :2551-10-30 11:15:23.9000
## B-thalassemia/H6E disease : 1 3rd Qu.:1 TPOG AML 03-08 : 40 3rd Qu.:2554-02-23 18:00:00.0 3rd Qu.:1.000 3rd Qu.:2554-06-15 00:00:00.0 3rd Qu.:2.000 3rd Qu.:2557-07-20 00:00:00.0000 3rd Qu.:2.000 3rd Qu.:2555-07-28 12:00:00.0000
## (Other) : 11 Max. :1 TPOG ALL VHR-08 : 38 Max. :2560-12-28 00:00:00.0 Max. :4.000 Max. :2561-02-05 00:00:00.0 Max. :2.000 Max. :2563-04-27 00:00:00.0000 Max. :2.000 Max. :2562-09-23 00:00:00.0000
## NA's :650 (Other) :229 NA's :121 NA's :357 NA's :464
## site...64 if combined...65 32. 2nd protocol chemo. if yes (protocol)...67 start date...68 32.1 remission if yes (date)...70 32.2 complete protocol if yes (date)...72 32.3 relapsed if yes (date)...74 site...75
## Min. :1.000 BM+CNS : 21 Min. :1.000 Refractory : 61 Min. :2542-11-12 00:00:00.0 Min. :1.000 Min. :2543-05-01 00:00:00.0000 Min. :1.000 Min. : 2 Min. :1.000 Min. :2543-12-18 00:00:00.0000 Min. :1.000
## 1st Qu.:1.000 BM+testis: 6 1st Qu.:1.000 ANLL-BFM 83 : 14 1st Qu.:2547-12-01 06:00:00.0 1st Qu.:1.000 1st Qu.:2548-06-02 00:00:00.0000 1st Qu.:2.000 1st Qu.:237732 1st Qu.:1.000 1st Qu.:2548-08-07 18:00:00.0000 1st Qu.:1.000
## Median :1.000 BM+Testis: 5 Median :2.000 TPOG AML 03-08 : 14 Median :2551-11-12 12:00:00.0 Median :2.000 Median :2552-06-13 00:00:00.0000 Median :2.000 Median :238193 Median :2.000 Median :2552-07-06 00:00:00.0000 Median :1.000
## Mean :1.602 CNS+BM : 2 Mean :1.747 Refractory Protocol: 8 Mean :2551-11-20 11:26:53.7 Mean :1.913 Mean :2552-08-12 02:30:41.9000 Mean :1.917 Mean :223210 Mean :1.653 Mean :2552-01-25 22:20:41.4000 Mean :1.403
## 3rd Qu.:2.000 NA's :656 3rd Qu.:2.000 Refractory protocol: 7 3rd Qu.:2555-08-03 18:00:00.0 3rd Qu.:3.000 3rd Qu.:2556-11-23 00:00:00.0000 3rd Qu.:2.000 3rd Qu.:240742 3rd Qu.:2.000 3rd Qu.:2554-10-21 00:00:00.0000 3rd Qu.:1.000
## Max. :4.000 Max. :2.000 (Other) : 71 Max. :2562-10-01 00:00:00.0 Max. :4.000 Max. :2562-12-11 00:00:00.0000 Max. :2.000 Max. :241911 Max. :2.000 Max. :2563-01-27 00:00:00.0000 Max. :4.000
## NA's :464 NA's :2 NA's :515 NA's :516 NA's :517 NA's :604 NA's :521 NA's :675 NA's :523 NA's :632 NA's :633
## if combined...76 33. status if death, cause date of death 34. Last FU ...81 start end 1. Site number Dx Others...8 A_CMT A_protocol
## BM+CNS : 2 Min. :1.000 Min. :1.000 Min. :2541-03-26 00:00:00.0000 Min. :2541-03-26 00:00:00.0000 refer : 7 Min. :2021-09-25 23:33:25.00 Min. :2021-09-25 23:38:13.14 Mode:logical 76.1. ALL:487 Mode:logical 1. Yes:690 MCCG 106 :176
## BM+CNS palliative: 1 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2547-10-28 06:00:00.0000 1st Qu.:2550-10-19 00:00:00.0000 turn CML : 1 1st Qu.:2021-09-30 02:37:41.76 1st Qu.:2021-09-30 08:44:16.07 NA's:690 76.2. AML:203 NA's:690 BFM 83 : 99
## CNS+BM : 1 Median :4.000 Median :1.000 Median :2551-07-15 12:00:00.0000 Median :2558-11-12 00:00:00.0000 รักษาต่อ ตปท.: 1 Median :2021-10-09 14:01:16.80 Median :2021-10-09 15:21:23.00 TPOG-ALL-01-05 : 76
## NA's :686 Mean :3.093 Mean :1.208 Mean :2551-08-10 03:29:31.4000 Mean :2556-10-15 15:49:33.9000 NA's :681 Mean :2021-10-15 19:28:13.59 Mean :2021-10-16 03:47:26.78 ThaiPOG-ALL-01-05: 53
## 3rd Qu.:4.000 3rd Qu.:1.000 3rd Qu.:2555-02-10 06:00:00.0000 3rd Qu.:2563-06-01 00:00:00.0000 3rd Qu.:2021-10-13 22:44:43.00 3rd Qu.:2021-10-13 23:03:14.50 TPOG-ALL-02-05 : 40
## Max. :4.000 Max. :3.000 Max. :2563-05-30 00:00:00.0000 Max. :2563-06-01 00:00:00.0000 Max. :2022-01-03 22:08:09.57 Max. :2022-01-03 22:11:35.82 TPOG-ALL-1301 : 31
## NA's :315 NA's :312 (Other) :215
## A_date A_Remiss A_Remiss_day A_Comp A_LastCMT A_Relapsed A_Relapse_date A_Site_relapsed A_BM A_CNS A_testis A_ Combine
## Min. :1991-05-30 00:00:00.00 1. Yes :537 Min. :1998-02-19 00:00:00.00 1. Yes:337 Min. :1998-09-21 00:00:00.00 1. Yes:225 Min. :1998-11-10 00:00:00.00 1. BM :152 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:2003-02-15 00:00:00.00 2. No : 67 1st Qu.:2003-06-27 12:00:00.00 2. No :352 1st Qu.:2007-11-05 00:00:00.00 2. No :464 1st Qu.:2004-12-21 00:00:00.00 1. BM 2. CNS : 28 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :2007-05-11 00:00:00.00 3. Not access : 55 Median :2007-08-28 00:00:00.00 NA's : 1 Median :2011-04-04 00:00:00.00 NA's : 1 Median :2009-03-23 12:00:00.00 2. CNS : 20 Median :1.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :2007-07-11 06:40:20.96 4. Death before complete induction: 29 Mean :2007-11-08 20:57:18.80 Mean :2011-03-13 19:00:53.41 Mean :2009-02-07 16:55:42.85 1. BM 3. Testis: 10 Mean :0.8694 Mean :0.2297 Mean :0.0901 Mean :0.0045
## 3rd Qu.:2011-02-11 12:00:00.00 NA's : 2 3rd Qu.:2011-07-28 18:00:00.00 3rd Qu.:2015-06-22 00:00:00.00 3rd Qu.:2013-01-04 12:00:00.00 3. Testis : 7 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :2017-12-28 00:00:00.00 Max. :2021-11-12 00:00:00.00 Max. :2021-04-05 00:00:00.00 Max. :2022-04-05 00:00:00.00 (Other) : 5 Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :3 NA's :154 NA's :353 NA's :466 NA's :468 NA's :468 NA's :468 NA's :468 NA's :468
## A_combine_data A_type_relapsed B_CMT B_protocol B_date B_Remiss B_remiss_day B_Comp B_LastCMT B_relapsed B_relapsed_date
## LN : 1 1. T-cell : 10 1. Yes:215 BFM 83 : 43 Min. :1998-04-09 00:00:00.00 1. Yes : 93 Min. :1998-05-26 00:00:00.00 1. Yes: 24 Min. :2002-09-02 00:00:00 1. Yes: 71 Min. :1999-06-14 00:00:00.00
## NA's:689 2. B-cell :100 2. No :472 Refractory protocol : 43 1st Qu.:2003-12-18 12:00:00.00 2. No : 58 1st Qu.:2004-07-23 00:00:00.00 2. No :190 1st Qu.:2005-03-03 12:00:00 2. No :143 1st Qu.:2004-11-22 00:00:00.00
## 3. Myeloid series: 61 NA's : 3 Refractory protocol + MCCG 106: 18 Median :2008-01-30 00:00:00.00 3. Not assess : 42 Median :2007-11-20 00:00:00.00 NA's :476 Median :2008-12-15 00:00:00 NA's :476 Median :2007-11-20 00:00:00.00
## 4. N/A : 0 TPOG-ALL-1305 : 12 Mean :2008-03-16 08:22:19.52 4. Death before complete induction: 22 Mean :2008-09-02 14:27:05.80 Mean :2010-01-28 18:00:00 Mean :2008-07-04 16:41:44.34
## NA's :519 TPOG-AML-03-08 : 8 3rd Qu.:2012-04-22 00:00:00.00 NA's :475 3rd Qu.:2013-04-29 00:00:00.00 3rd Qu.:2015-10-14 00:00:00 3rd Qu.:2011-10-12 00:00:00.00
## (Other) : 91 Max. :2021-05-18 00:00:00.00 Max. :2019-11-12 00:00:00.00 Max. :2019-04-29 00:00:00 Max. :2020-07-24 00:00:00.00
## NA's :475 NA's :475 NA's :597 NA's :666 NA's :621
## B_Site_relapsed B_BM B_CNS B_testis B_ Combine B_type_relapsed C_CMT C_protocol C_date C_Remiss C_remiss_day C_Comp C_LastCMT
## 1. Bone marrow : 56 Min. :0.000 Min. :0.000 Min. :0.0000 Min. :0 1. T-cell : 1 1. Yes: 49 BFM 83 : 8 Min. :1999-07-21 00:00:00.00 1. Yes : 10 Min. :2000-05-01 1. Yes: 3 Min. :2004-03-01 00:00:00
## 1. Bone marrow 2. CNS : 2 1st Qu.:1.000 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0 2. B-cell : 28 2. No :639 Refractory protocol: 6 1st Qu.:2003-07-07 18:00:00.00 2. No : 17 1st Qu.:2003-03-25 2. No : 44 1st Qu.:2006-04-10 12:00:00
## 1. Bone marrow 3. Testis: 1 Median :1.000 Median :0.000 Median :0.0000 Median :0 3. Myeloid series: 21 NA's : 2 CCG 2891 : 3 Median :2007-07-17 00:00:00.00 3. Not access : 13 Median :2005-05-18 NA's :643 Median :2008-05-20 00:00:00
## 2. CNS : 10 Mean :0.831 Mean :0.169 Mean :0.0423 Mean :0 4. N/A : 21 TPOG-ALL-1305 : 3 Mean :2008-02-26 06:46:57.39 4. Death before complete induction: 6 Mean :2007-08-27 Mean :2009-11-14 00:00:00
## 3. Testis : 2 3rd Qu.:1.000 3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.:0 NA's :619 UKALLR1 : 3 3rd Qu.:2012-02-25 12:00:00.00 NA's :644 3rd Qu.:2013-02-19 3rd Qu.:2012-09-21 00:00:00
## NA's :619 Max. :1.000 Max. :1.000 Max. :1.0000 Max. :0 (Other) : 26 Max. :2019-11-07 00:00:00.00 Max. :2019-12-16 Max. :2017-01-23 00:00:00
## NA's :619 NA's :619 NA's :619 NA's :619 NA's :641 NA's :644 NA's :681 NA's :687
## C_relapsed C_relapsed_date C_Site_relapsed C_BM C_CNS C_testis C_ Combine C_type_relapsed Status death_cause death_date Last_date CNS_diagnosis
## 1. Yes: 10 Min. :2000-01-31 00:00:00 1. BM : 8 Min. :0.0 Min. :0.0 Min. :0 Min. :0 2. B-cell : 2 1. Alive on chemotherapy : 3 1. Cancer related:255 Min. :1998-03-03 Min. :1999-12-08 1. CNS 1 :375
## 2. No : 37 1st Qu.:2003-05-12 12:00:00 1. BM 2. CNS: 1 1st Qu.:1.0 1st Qu.:0.0 1st Qu.:0 1st Qu.:0 3. Myeloid series: 5 2. Alive off chemotherapy :319 2. Complication :112 1st Qu.:2004-11-23 1st Qu.:2021-06-07 2. CNS 2 : 2
## NA's :643 Median :2008-08-13 00:00:00 2. CNS : 1 Median :1.0 Median :0.0 Median :0 Median :0 4. N/A : 2 3. Alive with alternative treatment: 1 NA's :323 Median :2008-11-24 Median :2021-07-06 3. CNS 3 : 26
## Mean :2007-06-27 00:00:00 NA's :680 Mean :0.9 Mean :0.2 Mean :0 Mean :0 NA's :681 4. Death :367 Mean :2009-02-10 Mean :2020-04-03 4. Traumatic trap: 7
## 3rd Qu.:2012-01-23 00:00:00 3rd Qu.:1.0 3rd Qu.:0.0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:2012-10-03 3rd Qu.:2021-07-06 5. Not evaluated :249
## Max. :2013-10-06 00:00:00 Max. :1.0 Max. :1.0 Max. :0 Max. :0 Max. :2021-07-28 Max. :2021-12-27 NA's : 31
## NA's :680 NA's :680 NA's :680 NA's :680 NA's :680 NA's :323 NA's :339
## 67. Metabolic complication TLS 68. Neurological complication...63 Seizure ICH Ischemic_stroke IICP Imaging Surgery Finding Operation 69. Respiratory complication Respiratory _status
## Mode:logical 1. Yes: 78 Mode:logical 1. Yes: 29 1. Yes: 8 1. Yes: 4 1. Yes: 14 1. Yes: 30 1. Yes: 1 A large area hypodense lesion at Lt frontoparietal: 1 Decompressive craniotomy: 1 Mode:logical 1. Yes: 89
## NA's:690 2. No :590 NA's:690 2. No :633 2. No :656 2. No :660 2. No :649 2. No :634 2. No :663 Acute dural venous sinus thrombosis : 1 NA's :689 NA's:690 2. No :575
## NA's : 22 NA's : 28 NA's : 26 NA's : 26 NA's : 27 NA's : 26 NA's : 26 aSDH,MLS, uncle herniation : 1 NA's : 26
## CNS ALL : 1
## Cortex atrophy, not seen infarct : 1
## (Other) : 24
## NA's :661
## 69.2 Diagnosis Pneumonia Pleural_effusion ARDS Pul_leuko Pul_ hem Others...80 Others_data DIC 71. Renal complication Renal_insuf RRT RRT_med 72. Infection Infect Sepsis
## Mode:logical 1. Yes: 76 1. Yes: 24 1. Yes: 21 2. No:662 1. Yes: 12 1. Yes: 7 pneumothorax : 2 70.1 Yes :114 Mode:logical 1. Yes: 41 1. Yes: 7 71.2.2 CAPD: 7 Mode:logical 1. Yes:585 1. Yes:165
## NA's:690 2. No :588 2. No :641 2. No :643 NA's : 28 2. No :649 2. No :657 Pneumothorax : 4 70.2 No :281 NA's:690 2. No :623 2. No :658 NA's :683 NA's:690 2. No : 80 2. No :484
## NA's : 26 NA's : 25 NA's : 26 NA's : 29 NA's : 26 Pulmonary edema: 1 70.3 No data:269 NA's : 26 NA's : 25 NA's : 25 NA's : 41
## NA's :683 NA's : 26
##
##
##
## Sepsis_ site site_ CNS Site_respi Site_CVS Site_GI Site_GU Site_skin Site_others site_ CNS_d Site_respi_d Site_CVS_d Site_GI_d Site_GU_d
## 72.1.7 Others :336 Min. :0 Min. :0.0000 Min. :0 Min. :0.0000 Min. :0.00000 Min. :0.0000 Min. :0.0000 Mode:logical pneumonia : 23 Mode:logical Fungal diarrhea : 13 UTI : 17
## 72.1.6 Skin and mucosa: phlebitis, abscess, candidiasis 72.1.7 Others: 59 1st Qu.:0 1st Qu.:0.0000 1st Qu.:0 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:1.0000 NA's:690 Pneumonia : 13 NA's:690 Diarrhea : 12 E. coli UTI : 5
## 72.1.4 GI: diarrhea, typhitis, liver abscess 72.1.7 Others : 45 Median :0 Median :0.0000 Median :0 Median :0.0000 Median :0.00000 Median :0.0000 Median :1.0000 IPA : 12 Infective diarrhea : 10 Enterococci UTI : 3
## 72.1.2 Lung: pneumonia, lung abscess, IPA 72.1.7 Others : 41 Mean :0 Mean :0.1402 Mean :0 Mean :0.1487 Mean :0.06325 Mean :0.1949 Mean :0.9214 Acute pharyngitis: 2 Sallmonella diarrhea: 6 KP UTI : 2
## 72.1.6 Skin and mucosa: phlebitis, abscess, candidiasis : 15 3rd Qu.:0 3rd Qu.:0.0000 3rd Qu.:0 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:1.0000 AOM : 2 Typhitis : 6 C. tropicalis UTI: 1
## (Other) : 89 Max. :0 Max. :1.0000 Max. :0 Max. :1.0000 Max. :1.00000 Max. :1.0000 Max. :1.0000 (Other) : 30 (Other) : 39 (Other) : 9
## NA's :105 NA's :105 NA's :105 NA's :105 NA's :105 NA's :105 NA's :105 NA's :105 NA's :608 NA's :604 NA's :653
## Site_skin_d Site_others _d 73. Management Intubation ICU 68. Neurological complication...108 _id _uuid _submission_time _validation_status _notes _status _submitted_by
## Thrombophlebitis: 29 FNP :394 Mode:logical 1. Yes: 79 1. Yes: 96 Mode:logical Min. :115986389 0001ad22-705f-49a7-8646-01b1090d4aef: 1 Min. :2021-09-25 16:38:24.00 Mode:logical Mode:logical submitted_via_web:690 Mode:logical
## Cellulitis : 10 E. coli septicemia : 7 NA's:690 2. No :585 2. No :569 NA's:690 1st Qu.:116742594 010ec787-ed83-45b1-b567-648183f2880f: 1 1st Qu.:2021-09-30 01:32:59.25 NA's:690 NA's:690 NA's:690
## Oral candidiasis: 8 Pseudomonas septicemia: 7 NA's : 26 NA's : 25 Median :118275362 02f799a4-4fa0-4d6a-be52-073751b078c0: 1 Median :2021-10-09 07:04:20.50
## Perianal abscess: 5 KP septicemia : 6 Mean :119355355 031966b1-ddf0-4ae5-a857-6918adcffedf: 1 Mean :2021-10-15 14:10:02.55
## oral candidiasis: 4 KP septicemia, FNP : 4 3rd Qu.:118973218 0325194d-7423-4329-b454-a6ab9a3a5b75: 1 3rd Qu.:2021-10-13 15:46:38.25
## (Other) : 58 (Other) :120 Max. :133448059 032b3eca-0d4b-47a3-add9-3d2f6b3c2915: 1 Max. :2022-01-03 15:11:47.00
## NA's :576 NA's :152 (Other) :684
## _tags _index hlk agegp sex rel_BM rel_CNS rel_Testis rdate outcome sdate stime event edate etime
## Mode:logical Min. : 1.0 No :568 0-23 : 89 Length:690 Yes:193 Yes: 51 Yes: 20 Min. :1998-11-10 Alive:323 Min. :1998-03-03 Min. : 0.005476 Mode :logical Min. :1998-03-03 Min. : 0.0000
## NA's:690 1st Qu.:181.2 Yes:122 24-49:204 Class :character No :497 No :639 No :670 1st Qu.:2004-07-09 Dead :367 1st Qu.:2008-04-22 1st Qu.: 1.106092 FALSE:294 1st Qu.:2007-08-21 1st Qu.: 0.7365
## Median :370.0 48-91:173 Mode :character Median :2008-08-12 Median :2017-11-10 Median : 4.360027 TRUE :396 Median :2015-11-04 Median : 3.5633
## Mean :367.5 92+ :224 Mean :2008-11-14 Mean :2014-11-13 Mean : 7.332885 Mean :2014-02-13 Mean : 6.5849
## 3rd Qu.:551.8 3rd Qu.:2012-08-21 3rd Qu.:2021-07-06 3rd Qu.:12.870637 3rd Qu.:2021-07-06 3rd Qu.:11.8898
## Max. :741.0 Max. :2022-04-05 Max. :2021-12-27 Max. :23.575633 Max. :2022-04-05 Max. :23.5756
## NA's :454
select(data, HN, A_dx, Last_date, Status, outcome, sdate, stime)
## # A tibble: 690 × 7
## HN A_dx Last_date Status outcome sdate stime
## <fct> <date> <date> <fct> <fct> <date> <dbl>
## 1 822768 1998-01-09 NA 4. Death Dead 2005-07-07 7.49
## 2 824206 1998-01-20 2021-07-06 2. Alive off chemotherapy Alive 2021-07-06 23.5
## 3 826638 1998-02-04 NA 4. Death Dead 1998-03-03 0.0739
## 4 829540 1998-02-24 NA 4. Death Dead 2006-06-17 8.31
## 5 829662 1998-02-24 2021-09-22 2. Alive off chemotherapy Alive 2021-09-22 23.6
## 6 832151 1998-03-12 NA 4. Death Dead 1998-06-11 0.249
## 7 832180 1998-03-12 NA 4. Death Dead 1998-10-10 0.580
## 8 834308 1998-03-30 NA 4. Death Dead 2000-04-12 2.04
## 9 835337 1998-04-08 2021-07-06 2. Alive off chemotherapy Alive 2021-07-06 23.2
## 10 837219 1998-04-20 2021-07-06 2. Alive off chemotherapy Alive 2021-07-06 23.2
## # … with 680 more rows
## # ℹ Use `print(n = ...)` to see more rows
count(data, Terticular_mass)
## # A tibble: 1 × 2
## Terticular_mass n
## <fct> <int>
## 1 No 690
# Omit variables that have no variation
data$Terticular_mass <- NULL
# Create BMI from height and weight
summary(data$HT)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 44.0 91.0 105.0 110.1 127.9 177.0
summary(data$BW)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.10 12.20 16.00 20.64 24.45 112.00
data <- mutate(data, bmi=BW/(HT/100)^2)
summary(data$bmi)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.774 13.773 14.943 15.644 16.716 44.461
# BMI
# https://www.cdc.gov/growthcharts/html_charts/bmiagerev.htm
# Data obtained from US CDC and WHO
# CDC (2-20 year-olds)
# https://www.cdc.gov/growthcharts/html_charts/bmiagerev.htm
# Males
BMI_male <- read.csv("D:/Consults/Thirachit/Pharsai/BMI_male.csv")
head(BMI_male)
## age p3 p5 p10 p25 p50 p75 p85 p90 p95 p97
## 1 24.0 14.52095 14.73732 15.09033 15.74164 16.57503 17.55719 18.16219 18.60948 19.33801 19.85986
## 2 24.5 14.50348 14.71929 15.07117 15.71963 16.54777 17.52129 18.11955 18.56111 19.27890 19.79194
## 3 25.5 14.46882 14.68361 15.03336 15.67634 16.49443 17.45135 18.03668 18.46730 19.16466 19.66102
## 4 26.5 14.43460 14.64843 14.99620 15.63403 16.44260 17.38384 17.95700 18.37736 19.05567 19.53658
## 5 27.5 14.40083 14.61379 14.95969 15.59268 16.39224 17.31871 17.88047 18.29125 18.95187 19.41849
## 6 28.5 14.36755 14.57969 14.92385 15.55226 16.34334 17.25593 17.80704 18.20892 18.85317 19.30665
tail(BMI_male)
## age p3 p5 p10 p25 p50 p75 p85 p90 p95 p97
## 214 236.5 18.59716 19.01129 19.70335 21.03795 22.86909 25.23883 26.83505 28.09363 30.30971 32.04453
## 215 237.5 18.62825 19.04343 19.73733 21.07579 22.91293 25.29179 26.89507 28.15978 30.38797 32.13348
## 216 238.5 18.65861 19.07484 19.77060 21.11296 22.95626 25.34459 26.95530 28.22650 30.46758 32.22457
## 217 239.5 18.68822 19.10551 19.80312 21.14946 22.99908 25.39725 27.01575 28.29381 30.54859 32.31787
## 218 240.0 18.70274 19.12055 19.81910 21.16745 23.02029 25.42353 27.04607 28.32770 30.58964 32.36537
## 219 240.5 18.71706 19.13540 19.83489 21.18526 23.04138 25.44978 27.07645 28.36174 30.63106 32.41344
# Omit first and last records and then round age down to nearest integer
BMI_males <- slice(BMI_male, -c(1,n())) %>%
mutate(age=trunc(age))
head(BMI_males)
## age p3 p5 p10 p25 p50 p75 p85 p90 p95 p97
## 1 24 14.50348 14.71929 15.07117 15.71963 16.54777 17.52129 18.11955 18.56111 19.27890 19.79194
## 2 25 14.46882 14.68361 15.03336 15.67634 16.49443 17.45135 18.03668 18.46730 19.16466 19.66102
## 3 26 14.43460 14.64843 14.99620 15.63403 16.44260 17.38384 17.95700 18.37736 19.05567 19.53658
## 4 27 14.40083 14.61379 14.95969 15.59268 16.39224 17.31871 17.88047 18.29125 18.95187 19.41849
## 5 28 14.36755 14.57969 14.92385 15.55226 16.34334 17.25593 17.80704 18.20892 18.85317 19.30665
## 6 29 14.33478 14.54615 14.88866 15.51275 16.29584 17.19546 17.73667 18.13031 18.75949 19.20097
tail(BMI_males)
## age p3 p5 p10 p25 p50 p75 p85 p90 p95 p97
## 212 235 18.56536 18.97844 19.66867 20.99946 22.82474 25.18572 26.77522 28.02801 30.23276 31.95764
## 213 236 18.59716 19.01129 19.70335 21.03795 22.86909 25.23883 26.83505 28.09363 30.30971 32.04453
## 214 237 18.62825 19.04343 19.73733 21.07579 22.91293 25.29179 26.89507 28.15978 30.38797 32.13348
## 215 238 18.65861 19.07484 19.77060 21.11296 22.95626 25.34459 26.95530 28.22650 30.46758 32.22457
## 216 239 18.68822 19.10551 19.80312 21.14946 22.99908 25.39725 27.01575 28.29381 30.54859 32.31787
## 217 240 18.70274 19.12055 19.81910 21.16745 23.02029 25.42353 27.04607 28.32770 30.58964 32.36537
# Females
BMI_female <- read.csv("D:/Consults/Thirachit/Pharsai/BMI_female.csv")
head(BMI_female)
## age p3 p5 p10 p25 p50 p75 p85 p90 p95 p97
## 1 24.0 14.14735 14.39787 14.80134 15.52808 16.42340 17.42746 18.01821 18.44139 19.10624 19.56411
## 2 24.5 14.13226 14.38019 14.77965 15.49976 16.38804 17.38582 17.97371 18.39526 19.05824 19.51534
## 3 25.5 14.10241 14.34527 14.73695 15.44422 16.31897 17.30485 17.88749 18.30611 18.96595 19.42198
## 4 26.5 14.07297 14.31097 14.69516 15.39015 16.25208 17.22693 17.80489 18.22103 18.87853 19.33410
## 5 27.5 14.04396 14.27728 14.65429 15.33754 16.18735 17.15202 17.72586 18.13997 18.79591 19.25163
## 6 28.5 14.01538 14.24420 14.61434 15.28640 16.12475 17.08009 17.65035 18.06285 18.71800 19.17448
tail(BMI_female)
## age p3 p5 p10 p25 p50 p75 p85 p90 p95 p97
## 214 236.5 17.43465 17.82119 18.47722 19.78248 21.67712 24.36130 26.36459 28.08801 31.53085 34.72503
## 215 237.5 17.43515 17.82256 18.48014 19.78887 21.68949 24.38426 26.39723 28.13034 31.59664 34.81810
## 216 238.5 17.43427 17.82259 18.48182 19.79420 21.70108 24.40686 26.42984 28.17291 31.66324 34.91250
## 217 239.5 17.43199 17.82127 18.48223 19.79846 21.71189 24.42910 26.46243 28.21574 31.73069 35.00831
## 218 240.0 17.43031 17.82009 18.48196 19.80018 21.71700 24.44010 26.47872 28.23727 31.76474 35.05675
## 219 240.5 17.42827 17.81856 18.48136 19.80162 21.72191 24.45101 26.49502 28.25888 31.79903 35.10556
# Omit first and last records and then round age down to nearest integer
BMI_females <- slice(BMI_female, -c(1,n())) %>%
mutate(age=trunc(age))
head(BMI_females)
## age p3 p5 p10 p25 p50 p75 p85 p90 p95 p97
## 1 24 14.13226 14.38019 14.77965 15.49976 16.38804 17.38582 17.97371 18.39526 19.05824 19.51534
## 2 25 14.10241 14.34527 14.73695 15.44422 16.31897 17.30485 17.88749 18.30611 18.96595 19.42198
## 3 26 14.07297 14.31097 14.69516 15.39015 16.25208 17.22693 17.80489 18.22103 18.87853 19.33410
## 4 27 14.04396 14.27728 14.65429 15.33754 16.18735 17.15202 17.72586 18.13997 18.79591 19.25163
## 5 28 14.01538 14.24420 14.61434 15.28640 16.12475 17.08009 17.65035 18.06285 18.71800 19.17448
## 6 29 13.98723 14.21175 14.57531 15.23671 16.06429 17.01107 17.57830 17.98962 18.64472 19.10255
tail(BMI_females)
## age p3 p5 p10 p25 p50 p75 p85 p90 p95 p97
## 212 235 17.43278 17.81852 18.47308 19.77505 21.66397 24.33798 26.33189 28.04591 31.46583 34.63326
## 213 236 17.43465 17.82119 18.47722 19.78248 21.67712 24.36130 26.36459 28.08801 31.53085 34.72503
## 214 237 17.43515 17.82256 18.48014 19.78887 21.68949 24.38426 26.39723 28.13034 31.59664 34.81810
## 215 238 17.43427 17.82259 18.48182 19.79420 21.70108 24.40686 26.42984 28.17291 31.66324 34.91250
## 216 239 17.43199 17.82127 18.48223 19.79846 21.71189 24.42910 26.46243 28.21574 31.73069 35.00831
## 217 240 17.43031 17.82009 18.48196 19.80018 21.71700 24.44010 26.47872 28.23727 31.76474 35.05675
# Combine males and females
BMI <- bind_rows(mutate(BMI_males, sex=factor("Male", levels=c("Male","Female"))),
mutate(BMI_females, sex=factor("Female", levels=c("Male","Female")))) %>%
select(age, sex, p5, p85, p95) %>%
rename(P5=p5, P85=p85, P95=p95)
head(BMI)
## age sex P5 P85 P95
## 1 24 Male 14.71929 18.11955 19.27890
## 2 25 Male 14.68361 18.03668 19.16466
## 3 26 Male 14.64843 17.95700 19.05567
## 4 27 Male 14.61379 17.88047 18.95187
## 5 28 Male 14.57969 17.80704 18.85317
## 6 29 Male 14.54615 17.73667 18.75949
tail(BMI)
## age sex P5 P85 P95
## 429 235 Female 17.81852 26.33189 31.46583
## 430 236 Female 17.82119 26.36459 31.53085
## 431 237 Female 17.82256 26.39723 31.59664
## 432 238 Female 17.82259 26.42984 31.66324
## 433 239 Female 17.82127 26.46243 31.73069
## 434 240 Female 17.82009 26.47872 31.76474
# Data for 0-2 years obtained from WHO:
# https://www.who.int/toolkits/child-growth-standards/standards/body-mass-index-for-age-bmi-for-age
# Males
BMI_0_2_m <- read_excel("D:/Consults/Thirachit/Pharsai/bfa-boys-percentiles-expanded-tables.xlsx") %>%
filter(Age %in% round(seq(0,931,30.4395))) %>%
mutate(age=round(Age/30.4395), sex="Male") %>%
select(age, sex, P5, P85, P95)
head(BMI_0_2_m)
## # A tibble: 6 × 5
## age sex P5 P85 P95
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 0 Male 11.5 14.8 15.8
## 2 1 Male 12.8 16.4 17.3
## 3 2 Male 14.1 17.9 18.8
## 4 3 Male 14.7 18.4 19.4
## 5 4 Male 15.0 18.7 19.7
## 6 5 Male 15.1 18.9 19.8
tail(BMI_0_2_m)
## # A tibble: 6 × 5
## age sex P5 P85 P95
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 25 Male 14.1 17.4 18.3
## 2 26 Male 14.1 17.3 18.2
## 3 27 Male 14.0 17.3 18.2
## 4 28 Male 14.0 17.2 18.1
## 5 29 Male 14.0 17.2 18.1
## 6 30 Male 13.9 17.2 18.0
# Females
BMI_0_2_f <- read_excel("D:/Consults/Thirachit/Pharsai/bfa-girls-percentiles-expanded-tables.xlsx") %>%
filter(Age %in% round(seq(0,931,30.4395))) %>%
mutate(age=round(Age/30.4395), sex="Female") %>%
select(age, sex, P5, P85, P95)
head(BMI_0_2_f)
## # A tibble: 6 × 5
## age sex P5 P85 P95
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 0 Female 11.5 14.7 15.5
## 2 1 Female 12.4 16.0 16.9
## 3 2 Female 13.5 17.4 18.4
## 4 3 Female 14.0 18.0 19.0
## 5 4 Female 14.3 18.3 19.4
## 6 5 Female 14.5 18.5 19.6
tail(BMI_0_2_f)
## # A tibble: 6 × 5
## age sex P5 P85 P95
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 25 Female 13.7 17.1 18.1
## 2 26 Female 13.7 17.1 18.1
## 3 27 Female 13.7 17.1 18.0
## 4 28 Female 13.6 17.0 18.0
## 5 29 Female 13.6 17.0 18.0
## 6 30 Female 13.6 17.0 17.9
BMI_0_2 <- bind_rows(BMI_0_2_m, BMI_0_2_f) %>%
filter()
head(BMI_0_2)
## # A tibble: 6 × 5
## age sex P5 P85 P95
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 0 Male 11.5 14.8 15.8
## 2 1 Male 12.8 16.4 17.3
## 3 2 Male 14.1 17.9 18.8
## 4 3 Male 14.7 18.4 19.4
## 5 4 Male 15.0 18.7 19.7
## 6 5 Male 15.1 18.9 19.8
tail(BMI_0_2)
## # A tibble: 6 × 5
## age sex P5 P85 P95
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 25 Female 13.7 17.1 18.1
## 2 26 Female 13.7 17.1 18.1
## 3 27 Female 13.7 17.1 18.0
## 4 28 Female 13.6 17.0 18.0
## 5 29 Female 13.6 17.0 18.0
## 6 30 Female 13.6 17.0 17.9
# Combine 0-24 months (0-2 years) with 24-240 months (2-10 years)
BMI <- bind_rows(BMI_0_2, BMI)
# Omit the duplicates due to the extra rows in the BMI data (due to the overlap)
BMI <- group_by(BMI, age, sex) %>%
slice(1) %>% ungroup()
head(BMI)
## # A tibble: 6 × 5
## age sex P5 P85 P95
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 0 Female 11.5 14.7 15.5
## 2 0 Male 11.5 14.8 15.8
## 3 1 Female 12.4 16.0 16.9
## 4 1 Male 12.8 16.4 17.3
## 5 2 Female 13.5 17.4 18.4
## 6 2 Male 14.1 17.9 18.8
tail(BMI)
## # A tibble: 6 × 5
## age sex P5 P85 P95
## <dbl> <chr> <dbl> <dbl> <dbl>
## 1 238 Female 17.8 26.4 31.7
## 2 238 Male 19.1 27.0 30.5
## 3 239 Female 17.8 26.5 31.7
## 4 239 Male 19.1 27.0 30.5
## 5 240 Female 17.8 26.5 31.8
## 6 240 Male 19.1 27.0 30.6
# Join to data on age and sex
data_BMI <- left_join(data, BMI, by=c("Age"="age", "sex"="sex")) %>%
# Create BMI status based on percentiles
mutate(bmi.gp=factor(
case_when(bmi>=P95~1, # Obese
bmi>=P85~2, # Overweight
bmi>=P5~3, # Normal (healthy)
bmi<P5~4, # Underweight
TRUE~NA_real_), # Missing
levels=1:4,
labels=c('Obese','Overweight','Normal','Underweight')))
# Reorder BMI levels
data <- mutate(data_BMI, bmi.gp=factor(bmi.gp, levels=c('Underweight','Normal','Overweight','Obese')))
count(data, bmi.gp)
## # A tibble: 4 × 2
## bmi.gp n
## <fct> <int>
## 1 Underweight 212
## 2 Normal 392
## 3 Overweight 44
## 4 Obese 42
# Early complications
data <- mutate(data,
early_com=ifelse(TLS=="1. Yes" |
Seizure=="1. Yes" |
ICH=="1. Yes" |
Ischemic_stroke=="1. Yes" |
IICP=="1. Yes" |
`Respiratory _status`=="1. Yes" |
Pneumonia=="1. Yes" |
Pleural_effusion=="1. Yes" |
ARDS=="1. Yes" |
Pul_leuko=="1. Yes" |
`Pul_ hem`=="1. Yes" |
DIC=="1. Yes" |
Renal_insuf=="1. Yes" |
RRT=="1. Yes" |
!is.na(RRT_med) |
Infect=="1. Yes" |
Sepsis=="1. Yes" |
!is.na(`Sepsis_ site`) |
Intubation=="1. Yes" |
ICU=="1. Yes", TRUE, FALSE))
data$early_com[is.na(data$early_com)] <- FALSE
count(data, early_com)
## # A tibble: 2 × 2
## early_com n
## <lgl> <int>
## 1 FALSE 96
## 2 TRUE 594
# Attach to the data to utilise epicalc functions
use(as.data.frame(data))
des()
##
## No. of observations = 690
## Variable Class Description
## 1 1.site factor
## 2 Year_diag numeric
## 3 No_year numeric
## 4 2.register no numeric
## 5 HN factor
## 6 Sex numeric
## 7 place numeric
## 8 dob POSIXct
## 9 A_ visit POSIXct
## 10 A_dx Date
## 11 Age numeric
## 12 BW numeric
## 13 HT numeric
## 14 Right numeric
## 15 Right_other factor
## 16 Mor factor
## 17 ALL factor
## 18 13.2ANLL numeric
## 19 Chro numeric
## 20 Chro_Others factor
## 21 15.1st CBC logical
## 22 wbc numeric
## 23 hb numeric
## 24 hct numeric
## 25 plt numeric
## 26 blast numeric
## 27 16.1st Serum chem logical
## 28 bun numeric
## 29 creatinine numeric
## 30 calcium numeric
## 31 phosphorus numeric
## 32 uric numeric
## 33 SGOT numeric
## 34 SGPT numeric
## 35 ALP numeric
## 36 LDH numeric
## 37 Mediastinal_mass factor
## 38 Fever factor
## 39 Arthitis factor
## 40 Arthitis_d factor
## 41 Xray factor
## 42 hepato factor
## 43 hepato_size numeric
## 44 Splenomegaly factor
## 45 Spleen_size numeric
## 46 lymphodenopathy factor
## 47 Exopthalmos factor
## 48 Skin_bleeding factor
## 49 Gum_hypertrophy factor
## 50 Bleed_gum factor
## 51 Jaundice factor
## 52 Underlying factor
## 53 Underlying_d factor
## 54 chemotherapy numeric
## 55 if yes (protocol)...56 factor
## 56 start date...57 POSIXct
## 57 31.1 remission numeric
## 58 if yes (date)...59 POSIXct
## 59 31.2 complete protocol numeric
## 60 if yes (date)...61 POSIXct
## 61 31.3 relapsed numeric
## 62 if yes (date)...63 POSIXct
## 63 site...64 numeric
## 64 if combined...65 factor
## 65 32. 2nd protocol chemo. numeric
## 66 if yes (protocol)...67 factor
## 67 start date...68 POSIXct
## 68 32.1 remission numeric
## 69 if yes (date)...70 POSIXct
## 70 32.2 complete protocol numeric
## 71 if yes (date)...72 numeric
## 72 32.3 relapsed numeric
## 73 if yes (date)...74 POSIXct
## 74 site...75 numeric
## 75 if combined...76 factor
## 76 33. status numeric
## 77 if death, cause numeric
## 78 date of death POSIXct
## 79 34. Last FU POSIXct
## 80 ...81 factor
## 81 start POSIXct
## 82 end POSIXct
## 83 1. Site number logical
## 84 Dx factor
## 85 Others...8 logical
## 86 A_CMT factor
## 87 A_protocol factor
## 88 A_date POSIXct
## 89 A_Remiss factor
## 90 A_Remiss_day POSIXct
## 91 A_Comp factor
## 92 A_LastCMT POSIXct
## 93 A_Relapsed factor
## 94 A_Relapse_date POSIXct
## 95 A_Site_relapsed factor
## 96 A_BM numeric
## 97 A_CNS numeric
## 98 A_testis numeric
## 99 A_ Combine numeric
## 100 A_combine_data factor
## 101 A_type_relapsed factor
## 102 B_CMT factor
## 103 B_protocol factor
## 104 B_date POSIXct
## 105 B_Remiss factor
## 106 B_remiss_day POSIXct
## 107 B_Comp factor
## 108 B_LastCMT POSIXct
## 109 B_relapsed factor
## 110 B_relapsed_date POSIXct
## 111 B_Site_relapsed factor
## 112 B_BM numeric
## 113 B_CNS numeric
## 114 B_testis numeric
## 115 B_ Combine numeric
## 116 B_type_relapsed factor
## 117 C_CMT factor
## 118 C_protocol factor
## 119 C_date POSIXct
## 120 C_Remiss factor
## 121 C_remiss_day POSIXct
## 122 C_Comp factor
## 123 C_LastCMT POSIXct
## 124 C_relapsed factor
## 125 C_relapsed_date POSIXct
## 126 C_Site_relapsed factor
## 127 C_BM numeric
## 128 C_CNS numeric
## 129 C_testis numeric
## 130 C_ Combine numeric
## 131 C_type_relapsed factor
## 132 Status factor
## 133 death_cause factor
## 134 death_date Date
## 135 Last_date Date
## 136 CNS_diagnosis factor
## 137 67. Metabolic complication logical
## 138 TLS factor
## 139 68. Neurological complication...63 logical
## 140 Seizure factor
## 141 ICH factor
## 142 Ischemic_stroke factor
## 143 IICP factor
## 144 Imaging factor
## 145 Surgery factor
## 146 Finding factor
## 147 Operation factor
## 148 69. Respiratory complication logical
## 149 Respiratory _status factor
## 150 69.2 Diagnosis logical
## 151 Pneumonia factor
## 152 Pleural_effusion factor
## 153 ARDS factor
## 154 Pul_leuko factor
## 155 Pul_ hem factor
## 156 Others...80 factor
## 157 Others_data factor
## 158 DIC factor
## 159 71. Renal complication logical
## 160 Renal_insuf factor
## 161 RRT factor
## 162 RRT_med factor
## 163 72. Infection logical
## 164 Infect factor
## 165 Sepsis factor
## 166 Sepsis_ site factor
## 167 site_ CNS numeric
## 168 Site_respi numeric
## 169 Site_CVS numeric
## 170 Site_GI numeric
## 171 Site_GU numeric
## 172 Site_skin numeric
## 173 Site_others numeric
## 174 site_ CNS_d logical
## 175 Site_respi_d factor
## 176 Site_CVS_d logical
## 177 Site_GI_d factor
## 178 Site_GU_d factor
## 179 Site_skin_d factor
## 180 Site_others _d factor
## 181 73. Management logical
## 182 Intubation factor
## 183 ICU factor
## 184 68. Neurological complication...108 logical
## 185 _id numeric
## 186 _uuid factor
## 187 _submission_time POSIXct
## 188 _validation_status logical
## 189 _notes logical
## 190 _status factor
## 191 _submitted_by logical
## 192 _tags logical
## 193 _index numeric
## 194 hlk factor
## 195 agegp factor
## 196 sex character
## 197 rel_BM factor
## 198 rel_CNS factor
## 199 rel_Testis factor
## 200 rdate Date
## 201 outcome factor
## 202 sdate Date
## 203 stime numeric
## 204 event logical
## 205 edate Date
## 206 etime numeric
## 207 bmi numeric
## 208 P5 numeric
## 209 P85 numeric
## 210 P95 numeric
## 211 bmi.gp factor
## 212 early_com logical
summ()
##
## No. of observations = 690
##
##
## Var. name obs. mean median s.d. min. max.
## 1 1.site 690 1 1 0 1 1
## 2 Year_diag 690 2550.05 2550 5.42 2541 2560
## 3 No_year 690 21.77 19.5 15.2 1 71
## 4 2.register no 619 1907369597783.05 1909803497561 405603052257.34 94021007440 5961200046342
## 5 HN 690 345.5 345.5 199.33 1 690
## 6 Sex 690 1.42 1 0.49 1 2
## 7 place 690 5.5 6 3.49 1 15
## 8 dob 690 2544-07-24 12:35 2544-11-09 12:00 <NA> 2526-09-19 00:00 2559-03-13 00:00
## 9 A_ visit 690 2550-07-10 19:09 2550-05-15 00:00 <NA> 2541-01-08 00:00 2560-12-26 00:00
## 10 A_dx 690 2007-07-14 2007-05-16 <NA> 1998-01-09 2017-12-28
## 11 Age 690 71.67 55.5 48 2 180
## 12 BW 690 20.64 16 13.62 2.1 112
## 13 HT 690 110.14 105 25.71 44 177
## 14 Right 690 1.1 1 0.34 1 4
## 15 Right_other 2 1.5 1.5 0.707 1 2
## 16 Mor 690 1.3 1 0.459 1 2
## 17 ALL 483 1.961 2 0.47 1 3
## 18 13.2ANLL 206 5.33 5 2 1 8
## 19 Chro 689 2.85 3 0.8 1 4
## 20 Chro_Others 143 71.154 71 40.856 1 141
## 21 15.1st CBC
## 22 wbc 690 66157.2 17525 128839.25 600 1251700
## 23 hb 690 7.42 7.3 3.19 1.4 64
## 24 hct 690 22.46 22 7.03 4.3 43
## 25 plt 690 72060.29 39500 92586.24 2000 823000
## 26 blast 689 48.15 49 34.85 0 100
## 27 16.1st Serum chem
## 28 bun 690 12.22 11.3 7.09 2.2 118.6
## 29 creatinine 690 0.55 0.5 0.36 0.13 6
## 30 calcium 690 9.48 9.4 1.01 4 18.4
## 31 phosphorus 690 4.77 4.8 1.29 0.31 19.3
## 32 uric 690 5.81 5.2 3.17 1.1 43.9
## 33 SGOT 690 51.31 32 74.2 7 1039
## 34 SGPT 690 30.99 15 57.89 2 640
## 35 ALP 690 167.21 141.5 107.27 4 1055
## 36 LDH 684 2555.35 1063.5 4643.32 11 46050
## 37 Mediastinal_mass 690 1.965 2 0.183 1 2
## 38 Fever 690 1.251 1 0.434 1 2
## 39 Arthitis 690 1.922 2 0.269 1 2
## 40 Arthitis_d 54 25.185 25.5 13.601 1 48
## 41 Xray 690 1.928 2 0.259 1 2
## 42 hepato 690 1.112 1 0.315 1 2
## 43 hepato_size 681 4.27 4 2.65 0 15
## 44 Splenomegaly 690 1.346 1 0.476 1 2
## 45 Spleen_size 635 3.41 3 3.18 0 16
## 46 lymphodenopathy 690 1.23 1 0.421 1 2
## 47 Exopthalmos 690 1.959 2 0.197 1 2
## 48 Skin_bleeding 690 1.59 2 0.492 1 2
## 49 Gum_hypertrophy 690 1.904 2 0.294 1 2
## 50 Bleed_gum 690 1.854 2 0.354 1 2
## 51 Jaundice 690 1.991 2 0.093 1 2
## 52 Underlying 690 1.943 2 0.231 1 2
## 53 Underlying_d 40 6.025 4 3.86 1 16
## 54 chemotherapy 690 1 1 0 1 1
## 55 if yes (protocol)...56 690 24.965 21 13.268 1 48
## 56 start date...57 690 2550-07-26 07:39 2550-05-27 00:00 <NA> 2541-01-14 00:00 2560-12-28 00:00
## 57 31.1 remission 690 1.35 1 0.84 1 4
## 58 if yes (date)...59 569 2550-09-30 04:13 2550-07-24 00:00 <NA> 2541-02-19 00:00 2561-02-05 00:00
## 59 31.2 complete protocol 690 1.52 2 0.5 1 2
## 60 if yes (date)...61 333 2553-10-23 21:58 2553-12-13 00:00 <NA> 2543-06-22 00:00 2563-04-27 00:00
## 61 31.3 relapsed 690 1.67 2 0.47 1 2
## 62 if yes (date)...63 226 2551-10-30 11:15 2551-10-15 00:00 <NA> 2542-06-14 00:00 2562-09-23 00:00
## 63 site...64 226 1.6 1 1.09 1 4
## 64 if combined...65 34 1.647 1 0.95 1 4
## 65 32. 2nd protocol chemo. 688 1.75 2 0.43 1 2
## 66 if yes (protocol)...67 175 23.623 22 11.591 1 45
## 67 start date...68 174 2551-11-20 11:26 2551-11-12 12:00 <NA> 2542-11-12 00:00 2562-10-01 00:00
## 68 32.1 remission 173 1.91 2 1.09 1 4
## 69 if yes (date)...70 86 2552-08-12 02:30 2552-06-13 00:00 <NA> 2543-05-01 00:00 2562-12-11 00:00
## 70 32.2 complete protocol 169 1.92 2 0.28 1 2
## 71 if yes (date)...72 15 223210.47 238193 61771.87 2 241911
## 72 32.3 relapsed 167 1.65 2 0.48 1 2
## 73 if yes (date)...74 58 2552-01-25 22:20 2552-07-06 00:00 <NA> 2543-12-18 00:00 2563-01-27 00:00
## 74 site...75 57 1.4 1 0.86 1 4
## 75 if combined...76 4 1.75 1.5 0.957 1 3
## 76 33. status 690 3.09 4 1.02 1 4
## 77 if death, cause 375 1.21 1 0.48 1 3
## 78 date of death 378 2551-08-10 03:29 2551-07-15 12:00 <NA> 2541-03-26 00:00 2563-05-30 00:00
## 79 34. Last FU 690 2556-10-15 15:49 2558-11-12 00:00 <NA> 2541-03-26 00:00 2563-06-01 00:00
## 80 ...81 9 1.333 1 0.707 1 3
## 81 start 690 2021-10-15 19:28 2021-10-09 14:01 <NA> 2021-09-25 23:33 2022-01-03 22:08
## 82 end 690 2021-10-16 03:47 2021-10-09 15:21 <NA> 2021-09-25 23:38 2022-01-03 22:11
## 83 1. Site number
## 84 Dx 690 1.294 1 0.456 1 2
## 85 Others...8
## 86 A_CMT 690 1 1 0 1 1
## 87 A_protocol 690 11.88 6 10.943 1 39
## 88 A_date 687 2007-07-11 06:40 2007-05-11 00:00 <NA> 1991-05-30 00:00 2017-12-28 00:00
## 89 A_Remiss 688 1.384 1 0.806 1 4
## 90 A_Remiss_day 536 2007-11-08 20:57 2007-08-28 00:00 <NA> 1998-02-19 00:00 2021-11-12 00:00
## 91 A_Comp 689 1.511 2 0.5 1 2
## 92 A_LastCMT 337 2011-03-13 19:00 2011-04-04 00:00 <NA> 1998-09-21 00:00 2021-04-05 00:00
## 93 A_Relapsed 689 1.673 2 0.469 1 2
## 94 A_Relapse_date 224 2009-02-07 16:55 2009-03-23 12:00 <NA> 1998-11-10 00:00 2022-04-05 00:00
## 95 A_Site_relapsed 222 2.167 1 2.258 1 9
## 96 A_BM 222 0.87 1 0.34 0 1
## 97 A_CNS 222 0.23 0 0.42 0 1
## 98 A_testis 222 0.09 0 0.29 0 1
## 99 A_ Combine 222 0 0 0.07 0 1
## 100 A_combine_data 1 1 1 <NA> 1 1
## 101 A_type_relapsed 171 2.298 2 0.573 1 3
## 102 B_CMT 687 1.687 2 0.464 1 2
## 103 B_protocol 215 20.507 16 14.265 1 49
## 104 B_date 215 2008-03-16 08:22 2008-01-30 00:00 <NA> 1998-04-09 00:00 2021-05-18 00:00
## 105 B_Remiss 215 1.967 2 1.02 1 4
## 106 B_remiss_day 93 2008-09-02 14:27 2007-11-20 00:00 <NA> 1998-05-26 00:00 2019-11-12 00:00
## 107 B_Comp 214 1.888 2 0.316 1 2
## 108 B_LastCMT 24 2010-01-28 18:00 2008-12-15 00:00 <NA> 2002-09-02 00:00 2019-04-29 00:00
## 109 B_relapsed 214 1.668 2 0.472 1 2
## 110 B_relapsed_date 69 2008-07-04 16:41 2007-11-20 00:00 <NA> 1999-06-14 00:00 2020-07-24 00:00
## 111 B_Site_relapsed 71 1.592 1 1.214 1 5
## 112 B_BM 71 0.83 1 0.38 0 1
## 113 B_CNS 71 0.17 0 0.38 0 1
## 114 B_testis 71 0.04 0 0.2 0 1
## 115 B_ Combine 71 0 0 0 0 0
## 116 B_type_relapsed 71 2.873 3 0.861 1 4
## 117 C_CMT 688 1.929 2 0.257 1 2
## 118 C_protocol 49 13.531 13 9.06 1 30
## 119 C_date 46 2008-02-26 06:46 2007-07-17 00:00 <NA> 1999-07-21 00:00 2019-11-07 00:00
## 120 C_Remiss 46 2.326 2 0.967 1 4
## 121 C_remiss_day 9 2007-08-27 00:00 2005-05-18 00:00 <NA> 2000-05-01 00:00 2019-12-16 00:00
## 122 C_Comp 47 1.936 2 0.247 1 2
## 123 C_LastCMT 3 2009-11-14 00:00 2008-05-20 00:00 <NA> 2004-03-01 00:00 2017-01-23 00:00
## 124 C_relapsed 47 1.787 2 0.414 1 2
## 125 C_relapsed_date 10 2007-06-27 00:00 2008-08-13 00:00 <NA> 2000-01-31 00:00 2013-10-06 00:00
## 126 C_Site_relapsed 10 1.3 1 0.675 1 3
## 127 C_BM 10 0.9 1 0.32 0 1
## 128 C_CNS 10 0.2 0 0.42 0 1
## 129 C_testis 10 0 0 0 0 0
## 130 C_ Combine 10 0 0 0 0 0
## 131 C_type_relapsed 9 2 2 0.707 1 3
## 132 Status 690 3.061 4 1.005 1 4
## 133 death_cause 367 1.305 1 0.461 1 2
## 134 death_date 367 2009-02-10 2008-11-24 <NA> 1998-03-03 2021-07-28
## 135 Last_date 351 2020-04-03 2021-07-06 <NA> 1999-12-08 2021-12-27
## 136 CNS_diagnosis 659 2.625 1 1.915 1 5
## 137 67. Metabolic complication
## 138 TLS 668 1.883 2 0.321 1 2
## 139 68. Neurological complication...63
## 140 Seizure 662 1.956 2 0.205 1 2
## 141 ICH 664 1.988 2 0.109 1 2
## 142 Ischemic_stroke 664 1.994 2 0.077 1 2
## 143 IICP 663 1.979 2 0.144 1 2
## 144 Imaging 664 1.955 2 0.208 1 2
## 145 Surgery 664 1.998 2 0.039 1 2
## 146 Finding 29 15 15 8.515 1 29
## 147 Operation 1 1 1 <NA> 1 1
## 148 69. Respiratory complication
## 149 Respiratory _status 664 1.866 2 0.341 1 2
## 150 69.2 Diagnosis
## 151 Pneumonia 664 1.886 2 0.319 1 2
## 152 Pleural_effusion 665 1.964 2 0.187 1 2
## 153 ARDS 664 1.968 2 0.175 1 2
## 154 Pul_leuko 662 1 1 0 1 1
## 155 Pul_ hem 661 1.982 2 0.134 1 2
## 156 Others...80 664 1.989 2 0.102 1 2
## 157 Others_data 7 1.857 2 0.69 1 3
## 158 DIC 664 2.233 2 0.723 1 3
## 159 71. Renal complication
## 160 Renal_insuf 664 1.938 2 0.241 1 2
## 161 RRT 665 1.989 2 0.102 1 2
## 162 RRT_med 7 1 1 0 1 1
## 163 72. Infection
## 164 Infect 665 1.12 1 0.326 1 2
## 165 Sepsis 649 1.746 2 0.436 1 2
## 166 Sepsis_ site 585 22.456 26 6.011 1 26
## 167 site_ CNS 585 0 0 0 0 0
## 168 Site_respi 585 0.14 0 0.35 0 1
## 169 Site_CVS 585 0 0 0 0 0
## 170 Site_GI 585 0.15 0 0.36 0 1
## 171 Site_GU 585 0.06 0 0.24 0 1
## 172 Site_skin 585 0.19 0 0.4 0 1
## 173 Site_others 585 0.92 1 0.27 0 1
## 174 site_ CNS_d
## 175 Site_respi_d 82 19.951 24 7.586 1 32
## 176 Site_CVS_d
## 177 Site_GI_d 86 15.174 13.5 7.929 1 30
## 178 Site_GU_d 37 9.892 12 4.557 1 14
## 179 Site_skin_d 114 33.219 36 16.66 1 55
## 180 Site_others _d 538 36.4 30 18.779 1 104
## 181 73. Management
## 182 Intubation 664 1.881 2 0.324 1 2
## 183 ICU 665 1.856 2 0.352 1 2
## 184 68. Neurological complication...108
## 185 _id 690 119355354.81 118275361.5 4223557.45 115986389 133448059
## 186 _uuid 690 345.5 345.5 199.33 1 690
## 187 _submission_time 690 2021-10-15 14:10 2021-10-09 07:04 <NA> 2021-09-25 16:38 2022-01-03 15:11
## 188 _validation_status
## 189 _notes
## 190 _status 690 1 1 0 1 1
## 191 _submitted_by
## 192 _tags
## 193 _index 690 367.54 370 213.1 1 741
## 194 hlk 690 1.177 1 0.382 1 2
## 195 agegp 690 2.771 3 1.042 1 4
## 196 sex
## 197 rel_BM 690 1.72 2 0.449 1 2
## 198 rel_CNS 690 1.926 2 0.262 1 2
## 199 rel_Testis 690 1.971 2 0.168 1 2
## 200 rdate 236 2008-11-14 2008-08-12 <NA> 1998-11-10 2022-04-05
## 201 outcome 690 1.532 2 0.499 1 2
## 202 sdate 690 2014-11-13 2017-11-10 <NA> 1998-03-03 2021-12-27
## 203 stime 690 7.33 4.36 7.08 0.01 23.58
## 204 event 690 0.57 1 0.49 0 1
## 205 edate 690 2014-02-13 2015-11-04 <NA> 1998-03-03 2022-04-05
## 206 etime 690 6.58 3.56 6.95 0 23.58
## 207 bmi 690 15.64 14.94 3.47 2.77 44.46
## 208 P5 690 14.19 14.01 0.67 13.42 16.55
## 209 P85 690 18.3 17.36 1.91 16.76 23.99
## 210 P95 690 20.04 18.46 2.82 17.82 28.05
## 211 bmi.gp 690 1.878 2 0.775 1 4
## 212 early_com 690 0.86 1 0.35 0 1
tableStack(vars=c(Age, agegp, sex, BW, HT, bmi.gp,
Mor, ALL,
hb:blast,
bun:LDH,
Mediastinal_mass:Arthitis,
Xray, hepato, Splenomegaly, lymphodenopathy, Exopthalmos,
Dx, # Why does this not correspond to Mor?
A_CMT,
TLS,Seizure,ICH,Ischemic_stroke, IICP, `Respiratory _status`, Pneumonia, Pleural_effusion, ARDS, Pul_leuko, `Pul_ hem`, DIC, Renal_insuf, RRT, RRT_med, Infect, Sepsis, Intubation, ICU, early_com),
by="none")
## Total
## Total 690
##
## Age
## Mean (SD) 71.7 (48.0)
## agegp
## 0-23 89 (12.9)
## 24-49 204 (29.6)
## 48-91 173 (25.1)
## 92+ 224 (32.5)
## sex
## Female 293 (42.5)
## Male 397 (57.5)
## BW
## Mean (SD) 20.6 (13.6)
## HT
## Mean (SD) 110.1 (25.7)
## bmi.gp
## Underweight 212 (30.7)
## Normal 392 (56.8)
## Overweight 44 (6.4)
## Obese 42 (6.1)
## Mor
## ALL 483 (70.0)
## AML 207 (30.0)
## ALL
## T_cell 63 (13.0)
## B-cell 376 (77.8)
## FAB 44 (9.1)
## hb
## Mean (SD) 7.4 (3.2)
## hct
## Mean (SD) 22.5 (7.0)
## plt
## Mean (SD) 72060.3 (92586.2)
## blast
## Mean (SD) 48.1 (34.9)
## bun
## Mean (SD) 12.2 (7.1)
## creatinine
## Mean (SD) 0.5 (0.4)
## calcium
## Mean (SD) 9.5 (1.0)
## phosphorus
## Mean (SD) 4.8 (1.3)
## uric
## Mean (SD) 5.8 (3.2)
## SGOT
## Mean (SD) 51.3 (74.2)
## SGPT
## Mean (SD) 31.0 (57.9)
## ALP
## Mean (SD) 167.2 (107.3)
## LDH
## Mean (SD) 2555.4 (4643.3)
## Mediastinal_mass
## Yes 24 (3.5)
## No 666 (96.5)
## Fever
## Yes 517 (74.9)
## No 173 (25.1)
## Arthitis
## Yes 54 (7.8)
## No 636 (92.2)
## Xray
## Yes 50 (7.2)
## No 640 (92.8)
## hepato
## Yes 613 (88.8)
## No 77 (11.2)
## Splenomegaly
## Yes 451 (65.4)
## No 239 (34.6)
## lymphodenopathy
## Yes 531 (77.0)
## No 159 (23.0)
## Exopthalmos
## Yes 28 (4.1)
## No 662 (95.9)
## Dx
## 76.1. ALL 487 (70.6)
## 76.2. AML 203 (29.4)
## A_CMT
## 1. Yes 690 (100.0)
## TLS
## 1. Yes 78 (11.7)
## 2. No 590 (88.3)
## Seizure
## 1. Yes 29 (4.4)
## 2. No 633 (95.6)
## ICH
## 1. Yes 8 (1.2)
## 2. No 656 (98.8)
## Ischemic_stroke
## 1. Yes 4 (0.6)
## 2. No 660 (99.4)
## IICP
## 1. Yes 14 (2.1)
## 2. No 649 (97.9)
## Respiratory _status
## 1. Yes 89 (13.4)
## 2. No 575 (86.6)
## Pneumonia
## 1. Yes 76 (11.4)
## 2. No 588 (88.6)
## Pleural_effusion
## 1. Yes 24 (3.6)
## 2. No 641 (96.4)
## ARDS
## 1. Yes 21 (3.2)
## 2. No 643 (96.8)
## Pul_leuko
## 2. No 662 (100.0)
## Pul_ hem
## 1. Yes 12 (1.8)
## 2. No 649 (98.2)
## DIC
## 70.1 Yes 114 (17.2)
## 70.2 No 281 (42.3)
## 70.3 No data 269 (40.5)
## Renal_insuf
## 1. Yes 41 (6.2)
## 2. No 623 (93.8)
## RRT
## 1. Yes 7 (1.1)
## 2. No 658 (98.9)
## RRT_med
## 71.2.2 CAPD 7 (100.0)
## Infect
## 1. Yes 585 (88.0)
## 2. No 80 (12.0)
## Sepsis
## 1. Yes 165 (25.4)
## 2. No 484 (74.6)
## Intubation
## 1. Yes 79 (11.9)
## 2. No 585 (88.1)
## ICU
## 1. Yes 96 (14.4)
## 2. No 569 (85.6)
## early_com
## No 96 (13.9)
## Yes 594 (86.1)
# Six cases have conflicting data
count(data, Dx, Mor)
## # A tibble: 4 × 3
## Dx Mor n
## <fct> <fct> <int>
## 1 76.1. ALL ALL 482
## 2 76.1. ALL AML 5
## 3 76.2. AML ALL 1
## 4 76.2. AML AML 202
filter(data, Dx=="76.1. ALL" & Mor=="AML" | Dx=="76.2. AML" & Mor=="ALL") %>%
select(5:10, Dx, Mor, ALL)
## # A tibble: 6 × 9
## HN Sex place dob `A_ visit` A_dx Dx Mor ALL
## <fct> <dbl> <dbl> <dttm> <dttm> <date> <fct> <fct> <fct>
## 1 949283 2 11 2536-10-05 00:00:00 2543-03-24 00:00:00 2000-03-28 76.1. ALL AML <NA>
## 2 1155703 2 2 2538-08-18 00:00:00 2548-07-07 00:00:00 2005-07-11 76.1. ALL AML <NA>
## 3 1279281 1 1 2544-07-13 00:00:00 2548-08-15 00:00:00 2005-08-19 76.1. ALL AML <NA>
## 4 1298714 1 11 2539-10-21 00:00:00 2548-12-06 00:00:00 2005-12-07 76.2. AML ALL B-cell
## 5 1337489 1 8 2547-06-04 00:00:00 2549-07-26 00:00:00 2006-07-26 76.1. ALL AML <NA>
## 6 1041881 1 3 2538-09-16 00:00:00 2552-01-20 00:00:00 2009-01-21 76.1. ALL AML <NA>
count(data, RRT_med, hlk)
## # A tibble: 4 × 3
## RRT_med hlk n
## <fct> <fct> <int>
## 1 71.2.2 CAPD No 4
## 2 71.2.2 CAPD Yes 3
## 3 <NA> No 564
## 4 <NA> Yes 119
count(data, Pul_leuko, hlk)
## # A tibble: 4 × 3
## Pul_leuko hlk n
## <fct> <fct> <int>
## 1 2. No No 548
## 2 2. No Yes 114
## 3 <NA> No 20
## 4 <NA> Yes 8
T1 <- tableStack(vars=c(Age,agegp, sex, BW, HT, bmi.gp,
Mor, ALL,
hb:blast,
bun:LDH,
Mediastinal_mass:Arthitis,
Xray, hepato, Splenomegaly, lymphodenopathy, Exopthalmos,
TLS,Seizure,ICH,Ischemic_stroke, IICP, `Respiratory _status`, Pneumonia, Pleural_effusion, ARDS, Pul_leuko, `Pul_ hem`, DIC, Renal_insuf, RRT, Infect, Sepsis, Intubation, ICU, early_com),
by=hlk, total.column = TRUE); T1
## No Yes Total Test stat. P value
## Total 568 122 690
##
## Age Ranksum test < 0.001
## Median (IQR) 53.0 (33.8,98.2) 95.5 (34,147.5) 55.5 (34,105.0)
## agegp Chisq. (3 df) = 41.16 < 0.001
## 0-23 67 (11.8) 22 (18.0) 89 (12.9)
## 24-49 184 (32.4) 20 (16.4) 204 (29.6)
## 48-91 158 (27.8) 15 (12.3) 173 (25.1)
## 92+ 159 (28.0) 65 (53.3) 224 (32.5)
## sex Chisq. (1 df) = 0.12 0.732
## Female 239 (42.1) 54 (44.3) 293 (42.5)
## Male 329 (57.9) 68 (55.7) 397 (57.5)
## BW Ranksum test < 0.001
## Median (IQR) 15.7 (12.2,22.1) 20.4 (12.2,37.9) 16.0 (12.2,24.4)
## HT Ranksum test < 0.001
## Median (IQR) 104.0 (91,124.5) 122.9 (92.2,150.0) 105.0 (91,127.9)
## bmi.gp Chisq. (3 df) = 1.99 0.574
## Underweight 179 (31.5) 33 (27.0) 212 (30.7)
## Normal 319 (56.2) 73 (59.8) 392 (56.8)
## Overweight 34 (6.0) 10 (8.2) 44 (6.4)
## Obese 36 (6.3) 6 (4.9) 42 (6.1)
## Mor Chisq. (1 df) = 1.14 0.286
## ALL 403 (71.0) 80 (65.6) 483 (70.0)
## AML 165 (29.0) 42 (34.4) 207 (30.0)
## ALL Chisq. (2 df) = 28.59 < 0.001
## T_cell 38 (9.4) 25 (31.2) 63 (13.0)
## B-cell 325 (80.6) 51 (63.7) 376 (77.8)
## FAB 40 (9.9) 4 (5.0) 44 (9.1)
## hb Ranksum test 0.09
## Median (IQR) 7.5 (5.7,9.1) 6.9 (5.5,8.4) 7.3 (5.6,9.1)
## hct t-test (688 df) = 1.22 0.222
## Mean (SD) 22.6 (7.0) 21.8 (7.0) 22.5 (7.0)
## plt Ranksum test 0.01
## Median (IQR) 41000.0 (18000,94250.0) 35000.0 (20000,56000.0) 39500.0 (19000,86000.0)
## blast Ranksum test < 0.001
## Median (IQR) 38.0 (8,68.0) 93.0 (87,96.0) 49.0 (13,85.0)
## bun Ranksum test 0.077
## Median (IQR) 11.4 (9,14.3) 10.4 (7.2,14.6) 11.3 (8.7,14.3)
## creatinine Ranksum test 0.036
## Median (IQR) 0.5 (0.4,0.6) 0.6 (0.4,0.8) 0.5 (0.4,0.7)
## calcium Ranksum test < 0.001
## Median (IQR) 9.5 (9,9.9) 9.2 (8.7,9.7) 9.4 (9,9.9)
## phosphorus Ranksum test < 0.001
## Median (IQR) 4.9 (4.2,5.5) 4.1 (3.3,5.1) 4.8 (4.1,5.5)
## uric Ranksum test < 0.001
## Median (IQR) 5.0 (3.8,6.6) 6.2 (4.6,9.0) 5.2 (3.9,6.8)
## SGOT Ranksum test < 0.001
## Median (IQR) 31.0 (23,45.2) 40.5 (26,71.0) 32.0 (24,49.0)
## SGPT Ranksum test 0.002
## Median (IQR) 15.0 (10,26.2) 19.5 (13,37.5) 15.0 (10,28.0)
## ALP Ranksum test < 0.001
## Median (IQR) 138.0 (106.8,182.2) 177.0 (111.5,257.0) 141.5 (107,193.0)
## LDH Ranksum test < 0.001
## Median (IQR) 928.0 (607,1921.0) 1968.0 (1175,4470.0) 1063.5 (644.5,2301.2)
## Mediastinal_mass Fisher's exact test 0.054
## Yes 16 (2.8) 8 (6.6) 24 (3.5)
## No 552 (97.2) 114 (93.4) 666 (96.5)
## Fever Chisq. (1 df) = 4.38 0.036
## Yes 416 (73.2) 101 (82.8) 517 (74.9)
## No 152 (26.8) 21 (17.2) 173 (25.1)
## Arthitis Chisq. (1 df) = 6.86 0.009
## Yes 52 (9.2) 2 (1.6) 54 (7.8)
## No 516 (90.8) 120 (98.4) 636 (92.2)
## Xray Chisq. (1 df) = 5.96 0.015
## Yes 48 (8.5) 2 (1.6) 50 (7.2)
## No 520 (91.5) 120 (98.4) 640 (92.8)
## hepato Chisq. (1 df) = 6.61 0.01
## Yes 496 (87.3) 117 (95.9) 613 (88.8)
## No 72 (12.7) 5 (4.1) 77 (11.2)
## Splenomegaly Chisq. (1 df) = 22.78 < 0.001
## Yes 348 (61.3) 103 (84.4) 451 (65.4)
## No 220 (38.7) 19 (15.6) 239 (34.6)
## lymphodenopathy Chisq. (1 df) = 6.32 0.012
## Yes 426 (75.0) 105 (86.1) 531 (77.0)
## No 142 (25.0) 17 (13.9) 159 (23.0)
## Exopthalmos Fisher's exact test 0.203
## Yes 26 (4.6) 2 (1.6) 28 (4.1)
## No 542 (95.4) 120 (98.4) 662 (95.9)
## TLS Chisq. (1 df) = 19.24 < 0.001
## 1. Yes 50 (9.1) 28 (23.9) 78 (11.7)
## 2. No 501 (90.9) 89 (76.1) 590 (88.3)
## Seizure Fisher's exact test 0.02
## 1. Yes 19 (3.5) 10 (8.8) 29 (4.4)
## 2. No 529 (96.5) 104 (91.2) 633 (95.6)
## ICH Fisher's exact test 0.005
## 1. Yes 3 (0.5) 5 (4.4) 8 (1.2)
## 2. No 547 (99.5) 109 (95.6) 656 (98.8)
## Ischemic_stroke Fisher's exact test 1
## 1. Yes 4 (0.7) 0 (0.0) 4 (0.6)
## 2. No 546 (99.3) 114 (100.0) 660 (99.4)
## IICP Fisher's exact test 0.073
## 1. Yes 9 (1.6) 5 (4.4) 14 (2.1)
## 2. No 541 (98.4) 108 (95.6) 649 (97.9)
## Respiratory _status Chisq. (1 df) = 2.49 0.115
## 1. Yes 68 (12.4) 21 (18.4) 89 (13.4)
## 2. No 482 (87.6) 93 (81.6) 575 (86.6)
## Pneumonia Chisq. (1 df) = 0.63 0.428
## 1. Yes 60 (10.9) 16 (14.0) 76 (11.4)
## 2. No 490 (89.1) 98 (86.0) 588 (88.6)
## Pleural_effusion Fisher's exact test 0.161
## 1. Yes 17 (3.1) 7 (6.1) 24 (3.6)
## 2. No 534 (96.9) 107 (93.9) 641 (96.4)
## ARDS Fisher's exact test 0.383
## 1. Yes 16 (2.9) 5 (4.4) 21 (3.2)
## 2. No 534 (97.1) 109 (95.6) 643 (96.8)
## Pul_leuko Chisq. (1 df) = 284.53 < 0.001
## 2. No 548 (100.0) 114 (100.0) 662 (100.0)
## Pul_ hem Fisher's exact test 0.444
## 1. Yes 9 (1.6) 3 (2.6) 12 (1.8)
## 2. No 538 (98.4) 111 (97.4) 649 (98.2)
## DIC Chisq. (2 df) = 19.76 < 0.001
## 70.1 Yes 80 (14.5) 34 (29.8) 114 (17.2)
## 70.2 No 231 (42.0) 50 (43.9) 281 (42.3)
## 70.3 No data 239 (43.5) 30 (26.3) 269 (40.5)
## Renal_insuf Chisq. (1 df) = 2.28 0.131
## 1. Yes 30 (5.4) 11 (9.7) 41 (6.2)
## 2. No 521 (94.6) 102 (90.3) 623 (93.8)
## RRT Fisher's exact test 0.102
## 1. Yes 4 (0.7) 3 (2.6) 7 (1.1)
## 2. No 547 (99.3) 111 (97.4) 658 (98.9)
## Infect Chisq. (1 df) = 5.21 0.023
## 1. Yes 477 (86.6) 108 (94.7) 585 (88.0)
## 2. No 74 (13.4) 6 (5.3) 80 (12.0)
## Sepsis Chisq. (1 df) = 4.2 0.04
## 1. Yes 128 (23.7) 37 (33.6) 165 (25.4)
## 2. No 411 (76.3) 73 (66.4) 484 (74.6)
## Intubation Chisq. (1 df) = 6.36 0.012
## 1. Yes 57 (10.4) 22 (19.3) 79 (11.9)
## 2. No 493 (89.6) 92 (80.7) 585 (88.1)
## ICU Chisq. (1 df) = 22.06 < 0.001
## 1. Yes 63 (11.4) 33 (28.9) 96 (14.4)
## 2. No 488 (88.6) 81 (71.1) 569 (85.6)
## early_com Chisq. (1 df) = 1.66 0.197
## No 84 (14.8) 12 (9.8) 96 (13.9)
## Yes 484 (85.2) 110 (90.2) 594 (86.1)
write.csv(T1, file="Table 1.csv")
count(data, Mor)
## # A tibble: 2 × 2
## Mor n
## <fct> <int>
## 1 ALL 483
## 2 AML 207
# Subgroups
data.ALL <- filter(data, Mor=="ALL")
data.AML <- filter(data, Mor=="AML")
use(data.frame(data.ALL))
T2a <- tableStack(vars=c(Age,agegp, sex, BW, HT, bmi.gp),
by=hlk, total.column = TRUE); T2a
## No Yes Total Test stat. P value
## Total 403 80 483
##
## Age Ranksum test 0.013
## Median (IQR) 52.0 (36,86.0) 91.0 (34,137.2) 54.0 (36,97.0)
## agegp Chisq. (3 df) = 34.18 < 0.001
## 0-23 33 (8.2) 13 (16.2) 46 (9.5)
## 24-49 145 (36.0) 16 (20.0) 161 (33.3)
## 48-91 130 (32.3) 11 (13.8) 141 (29.2)
## 92+ 95 (23.6) 40 (50.0) 135 (28.0)
## sex Chisq. (1 df) = 1.88 0.171
## Female 170 (42.2) 41 (51.2) 211 (43.7)
## Male 233 (57.8) 39 (48.8) 272 (56.3)
## BW Ranksum test 0.006
## Median (IQR) 15.7 (12.6,21.0) 20.1 (13.2,31.5) 16.0 (12.6,22.4)
## HT Ranksum test 0.009
## Median (IQR) 104.0 (92,119.8) 120.4 (93,140.2) 105.0 (92.5,124.8)
## bmi.gp Chisq. (3 df) = 4.09 0.251
## Underweight 128 (31.8) 18 (22.5) 146 (30.2)
## Normal 228 (56.6) 52 (65.0) 280 (58.0)
## Overweight 19 (4.7) 6 (7.5) 25 (5.2)
## Obese 28 (6.9) 4 (5.0) 32 (6.6)
write.csv(T2a, file="Table 2.csv")
T2b <- tableStack(vars=c(hb:blast,
bun:LDH,
Mediastinal_mass:Arthitis,
Xray, hepato, Splenomegaly, lymphodenopathy, Exopthalmos,
Mediastinal_mass:Arthitis,
Xray, hepato, Splenomegaly, lymphodenopathy, Exopthalmos),
by=hlk, total.column = TRUE, iqr="none"); T2b
## No Yes Total Test stat. P value
## Total 403 80 483
##
## hb t-test (481 df) = 1.22 0.223
## Mean (SD) 7.6 (3.7) 7.0 (2.7) 7.5 (3.5)
## hct t-test (481 df) = 1.2 0.231
## Mean (SD) 22.7 (7.1) 21.6 (7.9) 22.5 (7.3)
## plt t-test (481 df) = 4.04 < 0.001
## Mean (SD) 82901.0 (98461.0) 37975.0 (29874.8) 75459.8 (92256.7)
## blast t-test (480 df) = 13.23 < 0.001
## Mean (SD) 42.2 (32.2) 90.4 (11.5) 50.2 (34.8)
## bun t-test (481 df) = 0.06 0.952
## Mean (SD) 12.7 (7.8) 12.6 (7.7) 12.6 (7.7)
## creatinine t-test (481 df) = 0.66 0.512
## Mean (SD) 0.5 (0.4) 0.6 (0.3) 0.5 (0.4)
## calcium t-test (481 df) = 2.38 0.018
## Mean (SD) 9.6 (1.1) 9.3 (0.9) 9.6 (1.1)
## phosphorus t-test (481 df) = 5.12 < 0.001
## Mean (SD) 5.0 (1.3) 4.1 (1.5) 4.8 (1.3)
## uric t-test (481 df) = 3.91 < 0.001
## Mean (SD) 5.9 (3.3) 7.5 (4.1) 6.1 (3.5)
## SGOT t-test (481 df) = 3.17 0.002
## Mean (SD) 50.5 (69.8) 78.3 (82.2) 55.1 (72.6)
## SGPT t-test (481 df) = 0.48 0.631
## Mean (SD) 32.2 (61.0) 35.6 (31.9) 32.8 (57.2)
## ALP t-test (481 df) = 3.87 < 0.001
## Mean (SD) 167.7 (112.8) 221.1 (112.7) 176.6 (114.4)
## LDH t-test (478 df) = 4.03 < 0.001
## Mean (SD) 2350.3 (4820.7) 4971.5 (7105.8) 2776.3 (5339.4)
## Mediastinal_mass Fisher's exact test 0.037
## Yes 15 (3.7) 8 (10.0) 23 (4.8)
## No 388 (96.3) 72 (90.0) 460 (95.2)
## Fever Chisq. (1 df) = 0.28 0.599
## Yes 298 (73.9) 62 (77.5) 360 (74.5)
## No 105 (26.1) 18 (22.5) 123 (25.5)
## Arthitis Chisq. (1 df) = 6.74 0.009
## Yes 46 (11.4) 1 (1.2) 47 (9.7)
## No 357 (88.6) 79 (98.8) 436 (90.3)
## Xray Chisq. (1 df) = 6.06 0.014
## Yes 43 (10.7) 1 (1.2) 44 (9.1)
## No 360 (89.3) 79 (98.8) 439 (90.9)
## hepato Chisq. (1 df) = 6.25 0.012
## Yes 368 (91.3) 80 (100.0) 448 (92.8)
## No 35 (8.7) 0 (0.0) 35 (7.2)
## Splenomegaly Chisq. (1 df) = 19 < 0.001
## Yes 260 (64.5) 72 (90.0) 332 (68.7)
## No 143 (35.5) 8 (10.0) 151 (31.3)
## lymphodenopathy Chisq. (1 df) = 2.46 0.117
## Yes 319 (79.2) 70 (87.5) 389 (80.5)
## No 84 (20.8) 10 (12.5) 94 (19.5)
## Exopthalmos Fisher's exact test 0.704
## Yes 12 (3.0) 1 (1.2) 13 (2.7)
## No 391 (97.0) 79 (98.8) 470 (97.3)
## Mediastinal_mass Fisher's exact test 0.037
## Yes 15 (3.7) 8 (10.0) 23 (4.8)
## No 388 (96.3) 72 (90.0) 460 (95.2)
## Fever Chisq. (1 df) = 0.28 0.599
## Yes 298 (73.9) 62 (77.5) 360 (74.5)
## No 105 (26.1) 18 (22.5) 123 (25.5)
## Arthitis Chisq. (1 df) = 6.74 0.009
## Yes 46 (11.4) 1 (1.2) 47 (9.7)
## No 357 (88.6) 79 (98.8) 436 (90.3)
## Xray Chisq. (1 df) = 6.06 0.014
## Yes 43 (10.7) 1 (1.2) 44 (9.1)
## No 360 (89.3) 79 (98.8) 439 (90.9)
## hepato Chisq. (1 df) = 6.25 0.012
## Yes 368 (91.3) 80 (100.0) 448 (92.8)
## No 35 (8.7) 0 (0.0) 35 (7.2)
## Splenomegaly Chisq. (1 df) = 19 < 0.001
## Yes 260 (64.5) 72 (90.0) 332 (68.7)
## No 143 (35.5) 8 (10.0) 151 (31.3)
## lymphodenopathy Chisq. (1 df) = 2.46 0.117
## Yes 319 (79.2) 70 (87.5) 389 (80.5)
## No 84 (20.8) 10 (12.5) 94 (19.5)
## Exopthalmos Fisher's exact test 0.704
## Yes 12 (3.0) 1 (1.2) 13 (2.7)
## No 391 (97.0) 79 (98.8) 470 (97.3)
write.csv(T2b, file="Table 2b.csv")
T2c <- tableStack(vars=c(early_com,
TLS,Seizure, ICH, Ischemic_stroke, IICP, `Respiratory._status`,
Pneumonia, Pleural_effusion, ARDS, Pul_leuko, `Pul_.hem`, DIC,
Renal_insuf, RRT, Infect, Sepsis, Intubation, ICU),
by=hlk, total.column = TRUE); T2c
## No Yes Total Test stat. P value
## Total 403 80 483
##
## early_com Chisq. (1 df) = 2.93 0.087
## FALSE 69 (17.1) 7 (8.8) 76 (15.7)
## TRUE 334 (82.9) 73 (91.2) 407 (84.3)
## TLS Chisq. (1 df) = 19.83 < 0.001
## 1. Yes 43 (11.0) 24 (31.2) 67 (14.3)
## 2. No 349 (89.0) 53 (68.8) 402 (85.7)
## Seizure Fisher's exact test 0.003
## 1. Yes 12 (3.1) 9 (12.0) 21 (4.6)
## 2. No 374 (96.9) 66 (88.0) 440 (95.4)
## ICH Fisher's exact test 0.056
## 1. Yes 3 (0.8) 3 (4.0) 6 (1.3)
## 2. No 386 (99.2) 72 (96.0) 458 (98.7)
## Ischemic_stroke Fisher's exact test 1
## 1. Yes 2 (0.5) 0 (0.0) 2 (0.4)
## 2. No 386 (99.5) 75 (100.0) 461 (99.6)
## IICP Fisher's exact test 0.041
## 1. Yes 5 (1.3) 4 (5.4) 9 (1.9)
## 2. No 383 (98.7) 70 (94.6) 453 (98.1)
## Respiratory._status Chisq. (1 df) = 8.27 0.004
## 1. Yes 32 (8.2) 15 (20.0) 47 (10.2)
## 2. No 356 (91.8) 60 (80.0) 416 (89.8)
## Pneumonia Chisq. (1 df) = 1.58 0.209
## 1. Yes 27 (7.0) 9 (12.0) 36 (7.8)
## 2. No 361 (93.0) 66 (88.0) 427 (92.2)
## Pleural_effusion Fisher's exact test 0.054
## 1. Yes 12 (3.1) 6 (8.0) 18 (3.9)
## 2. No 377 (96.9) 69 (92.0) 446 (96.1)
## ARDS Fisher's exact test 0.21
## 1. Yes 7 (1.8) 3 (4.0) 10 (2.2)
## 2. No 381 (98.2) 72 (96.0) 453 (97.8)
## Pul_leuko Chisq. (1 df) = 211.6 < 0.001
## 2. No 388 (100.0) 75 (100.0) 463 (100.0)
## Pul_.hem Fisher's exact test 0.189
## 1. Yes 3 (0.8) 2 (2.7) 5 (1.1)
## 2. No 382 (99.2) 73 (97.3) 455 (98.9)
## DIC Chisq. (2 df) = 18.02 < 0.001
## 70.1 Yes 44 (11.3) 21 (28.0) 65 (14.0)
## 70.2 No 173 (44.5) 35 (46.7) 208 (44.8)
## 70.3 No data 172 (44.2) 19 (25.3) 191 (41.2)
## Renal_insuf Fisher's exact test 0.025
## 1. Yes 18 (4.6) 9 (12.2) 27 (5.8)
## 2. No 371 (95.4) 65 (87.8) 436 (94.2)
## RRT Fisher's exact test 0.056
## 1. Yes 3 (0.8) 3 (4.0) 6 (1.3)
## 2. No 386 (99.2) 72 (96.0) 458 (98.7)
## Infect Chisq. (1 df) = 4.96 0.026
## 1. Yes 327 (84.1) 71 (94.7) 398 (85.8)
## 2. No 62 (15.9) 4 (5.3) 66 (14.2)
## Sepsis Chisq. (1 df) = 6.83 0.009
## 1. Yes 80 (21.1) 26 (36.1) 106 (23.5)
## 2. No 300 (78.9) 46 (63.9) 346 (76.5)
## Intubation Chisq. (1 df) = 11.45 < 0.001
## 1. Yes 24 (6.2) 14 (18.7) 38 (8.2)
## 2. No 365 (93.8) 61 (81.3) 426 (91.8)
## ICU Chisq. (1 df) = 29.78 < 0.001
## 1. Yes 28 (7.2) 22 (29.3) 50 (10.8)
## 2. No 361 (92.8) 53 (70.7) 414 (89.2)
write.csv(T2c, file="Table 2c.csv")
# Table 3 (ALL)
T3 <- tableStack(vars=c(A_Remiss, A_Relapsed, rel_BM, rel_CNS, rel_Testis, A_type_relapsed, Status, outcome),
by=hlk, total.column = TRUE); T3
## A_type_relapsed has zero count in at least one row
## No Yes Total Test stat. P value
## Total 403 80 483
##
## A_Remiss Fisher's exact test 0.011
## 1. Yes 365 (90.8) 63 (79.7) 428 (89.0)
## 2. No 8 (2.0) 6 (7.6) 14 (2.9)
## 3. Not access 16 (4.0) 4 (5.1) 20 (4.2)
## 4. Death before complete induction 13 (3.2) 6 (7.6) 19 (4.0)
## A_Relapsed Chisq. (1 df) = 1.06 0.304
## 1. Yes 131 (32.5) 31 (39.2) 162 (33.6)
## 2. No 272 (67.5) 48 (60.8) 320 (66.4)
## rel_BM Chisq. (1 df) = 0.82 0.366
## Yes 108 (26.8) 26 (32.5) 134 (27.7)
## No 295 (73.2) 54 (67.5) 349 (72.3)
## rel_CNS Chisq. (1 df) = 0.19 0.659
## Yes 36 (8.9) 9 (11.2) 45 (9.3)
## No 367 (91.1) 71 (88.8) 438 (90.7)
## rel_Testis Fisher's exact test 0.339
## Yes 18 (4.5) 1 (1.2) 19 (3.9)
## No 385 (95.5) 79 (98.8) 464 (96.1)
## A_type_relapsed Fisher's exact test 0.164
## 1. T-cell 7 (8.0) 3 (11.5) 10 (8.8)
## 2. B-cell 79 (90.8) 21 (80.8) 100 (88.5)
## 3. Myeloid series 1 (1.1) 2 (7.7) 3 (2.7)
## 4. N/A 0 (0.0) 0 (0.0) 0 (0.0)
## Status Fisher's exact test < 0.001
## 1. Alive on chemotherapy 3 (0.7) 0 (0.0) 3 (0.6)
## 2. Alive off chemotherapy 246 (61.0) 26 (32.5) 272 (56.3)
## 3. Alive with alternative treatment 0 (0.0) 1 (1.2) 1 (0.2)
## 4. Death 154 (38.2) 53 (66.2) 207 (42.9)
## outcome Chisq. (1 df) = 20.3 < 0.001
## Alive 249 (61.8) 27 (33.8) 276 (57.1)
## Dead 154 (38.2) 53 (66.2) 207 (42.9)
write.csv(T3, file="Table 3.csv")
#
use(data.frame(data.AML))
T4a <- tableStack(vars=c(Age,agegp, sex, BW, HT, bmi.gp),
by=hlk, total.column = TRUE); T4a
## No Yes Total Test stat. P value
## Total 165 42 207
##
## Age Ranksum test 0.021
## Median (IQR) 58.0 (25,116.0) 137.0 (34,150.5) 65.0 (25.5,133.5)
## agegp Chisq. (3 df) = 7.77 0.051
## 0-23 34 (20.6) 9 (21.4) 43 (20.8)
## 24-49 39 (23.6) 4 (9.5) 43 (20.8)
## 48-91 28 (17.0) 4 (9.5) 32 (15.5)
## 92+ 64 (38.8) 25 (59.5) 89 (43.0)
## sex Chisq. (1 df) = 1.23 0.268
## Female 69 (41.8) 13 (31.0) 82 (39.6)
## Male 96 (58.2) 29 (69.0) 125 (60.4)
## BW Ranksum test 0.025
## Median (IQR) 15.8 (10.5,25.5) 27.2 (11,41.9) 16.9 (10.6,28.6)
## HT Ranksum test 0.011
## Median (IQR) 103.0 (83,131.0) 138.0 (88.8,153.0) 109.0 (83.2,137.2)
## bmi.gp Fisher's exact test 0.921
## Underweight 51 (30.9) 15 (35.7) 66 (31.9)
## Normal 91 (55.2) 21 (50.0) 112 (54.1)
## Overweight 15 (9.1) 4 (9.5) 19 (9.2)
## Obese 8 (4.8) 2 (4.8) 10 (4.8)
write.csv(T4a, file="Table 4a.csv")
T4b <- tableStack(vars=c(hb:blast,
bun:LDH,
Mediastinal_mass:Arthitis,
Xray, hepato, Splenomegaly, lymphodenopathy, Exopthalmos,
Mediastinal_mass:Arthitis,
Xray, hepato, Splenomegaly, lymphodenopathy, Exopthalmos),
by=hlk, total.column = TRUE, iqr="none"); T4b
## No Yes Total Test stat. P value
## Total 165 42 207
##
## hb t-test (205 df) = 0.55 0.585
## Mean (SD) 7.3 (2.3) 7.1 (1.6) 7.3 (2.2)
## hct t-test (205 df) = 0.37 0.713
## Mean (SD) 22.4 (6.8) 22.0 (4.9) 22.3 (6.4)
## plt t-test (205 df) = 1.1 0.272
## Mean (SD) 67724.2 (102520.2) 50000.0 (35262.8) 64128.0 (93091.5)
## blast t-test (205 df) = 9.2 < 0.001
## Mean (SD) 33.9 (30.3) 80.5 (24.7) 43.4 (34.7)
## bun t-test (205 df) = 2.14 0.033
## Mean (SD) 11.6 (5.1) 9.7 (5.0) 11.2 (5.1)
## creatinine t-test (205 df) = 1.71 0.089
## Mean (SD) 0.5 (0.2) 0.6 (0.3) 0.5 (0.2)
## calcium t-test (205 df) = 2.33 0.021
## Mean (SD) 9.3 (0.7) 9.0 (0.9) 9.3 (0.7)
## phosphorus t-test (205 df) = 3.75 < 0.001
## Mean (SD) 4.8 (1.0) 4.1 (1.4) 4.6 (1.2)
## uric t-test (205 df) = 3.55 < 0.001
## Mean (SD) 4.8 (1.9) 6.0 (2.6) 5.0 (2.1)
## SGOT t-test (205 df) = 0.38 0.702
## Mean (SD) 43.6 (85.0) 38.4 (31.7) 42.5 (77.2)
## SGPT t-test (205 df) = 1.04 0.302
## Mean (SD) 29.0 (66.0) 18.4 (13.2) 26.9 (59.3)
## ALP t-test (205 df) = 1.6 0.112
## Mean (SD) 140.6 (63.8) 164.0 (139.1) 145.4 (84.7)
## LDH t-test (202 df) = 1.91 0.058
## Mean (SD) 1888.6 (2135.3) 2619.7 (2425.5) 2035.6 (2210.0)
## Mediastinal_mass Fisher's exact test 1
## Yes 1 (0.6) 0 (0.0) 1 (0.5)
## No 164 (99.4) 42 (100.0) 206 (99.5)
## Fever Chisq. (1 df) = 7.2 0.007
## Yes 118 (71.5) 39 (92.9) 157 (75.8)
## No 47 (28.5) 3 (7.1) 50 (24.2)
## Arthitis Fisher's exact test 1
## Yes 6 (3.6) 1 (2.4) 7 (3.4)
## No 159 (96.4) 41 (97.6) 200 (96.6)
## Xray Fisher's exact test 1
## Yes 5 (3.0) 1 (2.4) 6 (2.9)
## No 160 (97.0) 41 (97.6) 201 (97.1)
## hepato Chisq. (1 df) = 1.69 0.194
## Yes 128 (77.6) 37 (88.1) 165 (79.7)
## No 37 (22.4) 5 (11.9) 42 (20.3)
## Splenomegaly Chisq. (1 df) = 4.94 0.026
## Yes 88 (53.3) 31 (73.8) 119 (57.5)
## No 77 (46.7) 11 (26.2) 88 (42.5)
## lymphodenopathy Chisq. (1 df) = 4.49 0.034
## Yes 107 (64.8) 35 (83.3) 142 (68.6)
## No 58 (35.2) 7 (16.7) 65 (31.4)
## Exopthalmos Fisher's exact test 0.314
## Yes 14 (8.5) 1 (2.4) 15 (7.2)
## No 151 (91.5) 41 (97.6) 192 (92.8)
## Mediastinal_mass Fisher's exact test 1
## Yes 1 (0.6) 0 (0.0) 1 (0.5)
## No 164 (99.4) 42 (100.0) 206 (99.5)
## Fever Chisq. (1 df) = 7.2 0.007
## Yes 118 (71.5) 39 (92.9) 157 (75.8)
## No 47 (28.5) 3 (7.1) 50 (24.2)
## Arthitis Fisher's exact test 1
## Yes 6 (3.6) 1 (2.4) 7 (3.4)
## No 159 (96.4) 41 (97.6) 200 (96.6)
## Xray Fisher's exact test 1
## Yes 5 (3.0) 1 (2.4) 6 (2.9)
## No 160 (97.0) 41 (97.6) 201 (97.1)
## hepato Chisq. (1 df) = 1.69 0.194
## Yes 128 (77.6) 37 (88.1) 165 (79.7)
## No 37 (22.4) 5 (11.9) 42 (20.3)
## Splenomegaly Chisq. (1 df) = 4.94 0.026
## Yes 88 (53.3) 31 (73.8) 119 (57.5)
## No 77 (46.7) 11 (26.2) 88 (42.5)
## lymphodenopathy Chisq. (1 df) = 4.49 0.034
## Yes 107 (64.8) 35 (83.3) 142 (68.6)
## No 58 (35.2) 7 (16.7) 65 (31.4)
## Exopthalmos Fisher's exact test 0.314
## Yes 14 (8.5) 1 (2.4) 15 (7.2)
## No 151 (91.5) 41 (97.6) 192 (92.8)
write.csv(T4b, file="Table 4b.csv")
T4c <- tableStack(vars=c(early_com,
TLS,Seizure, ICH, Ischemic_stroke, IICP, `Respiratory._status`,
Pneumonia, Pleural_effusion, ARDS, Pul_leuko, `Pul_.hem`, DIC,
Renal_insuf, RRT, Infect, Sepsis, Intubation, ICU),
by=hlk, total.column = TRUE); T4c
## No Yes Total Test stat. P value
## Total 165 42 207
##
## early_com Fisher's exact test 0.565
## FALSE 15 (9.1) 5 (11.9) 20 (9.7)
## TRUE 150 (90.9) 37 (88.1) 187 (90.3)
## TLS Fisher's exact test 0.236
## 1. Yes 7 (4.4) 4 (10.0) 11 (5.5)
## 2. No 152 (95.6) 36 (90.0) 188 (94.5)
## Seizure Fisher's exact test 1
## 1. Yes 7 (4.3) 1 (2.6) 8 (4.0)
## 2. No 155 (95.7) 38 (97.4) 193 (96.0)
## ICH Fisher's exact test 0.037
## 1. Yes 0 (0.0) 2 (5.1) 2 (1.0)
## 2. No 161 (100.0) 37 (94.9) 198 (99.0)
## Ischemic_stroke Fisher's exact test 1
## 1. Yes 2 (1.2) 0 (0.0) 2 (1.0)
## 2. No 160 (98.8) 39 (100.0) 199 (99.0)
## IICP Fisher's exact test 1
## 1. Yes 4 (2.5) 1 (2.6) 5 (2.5)
## 2. No 158 (97.5) 38 (97.4) 196 (97.5)
## Respiratory._status Chisq. (1 df) = 0.52 0.469
## 1. Yes 36 (22.2) 6 (15.4) 42 (20.9)
## 2. No 126 (77.8) 33 (84.6) 159 (79.1)
## Pneumonia Chisq. (1 df) = 0.01 0.907
## 1. Yes 33 (20.4) 7 (17.9) 40 (19.9)
## 2. No 129 (79.6) 32 (82.1) 161 (80.1)
## Pleural_effusion Fisher's exact test 1
## 1. Yes 5 (3.1) 1 (2.6) 6 (3.0)
## 2. No 157 (96.9) 38 (97.4) 195 (97.0)
## ARDS Fisher's exact test 1
## 1. Yes 9 (5.6) 2 (5.1) 11 (5.5)
## 2. No 153 (94.4) 37 (94.9) 190 (94.5)
## Pul_leuko Chisq. (1 df) = 73.57 < 0.001
## 2. No 160 (100.0) 39 (100.0) 199 (100.0)
## Pul_.hem Fisher's exact test 1
## 1. Yes 6 (3.7) 1 (2.6) 7 (3.5)
## 2. No 156 (96.3) 38 (97.4) 194 (96.5)
## DIC Chisq. (2 df) = 3.04 0.219
## 70.1 Yes 36 (22.4) 13 (33.3) 49 (24.5)
## 70.2 No 58 (36.0) 15 (38.5) 73 (36.5)
## 70.3 No data 67 (41.6) 11 (28.2) 78 (39.0)
## Renal_insuf Fisher's exact test 1
## 1. Yes 12 (7.4) 2 (5.1) 14 (7.0)
## 2. No 150 (92.6) 37 (94.9) 187 (93.0)
## RRT Fisher's exact test 1
## 1. Yes 1 (0.6) 0 (0.0) 1 (0.5)
## 2. No 161 (99.4) 39 (100.0) 200 (99.5)
## Infect Fisher's exact test 1
## 1. Yes 150 (92.6) 37 (94.9) 187 (93.0)
## 2. No 12 (7.4) 2 (5.1) 14 (7.0)
## Sepsis Chisq. (1 df) = 0 1
## 1. Yes 48 (30.2) 11 (28.9) 59 (29.9)
## 2. No 111 (69.8) 27 (71.1) 138 (70.1)
## Intubation Chisq. (1 df) = 0 1
## 1. Yes 33 (20.5) 8 (20.5) 41 (20.5)
## 2. No 128 (79.5) 31 (79.5) 159 (79.5)
## ICU Chisq. (1 df) = 0.45 0.504
## 1. Yes 35 (21.6) 11 (28.2) 46 (22.9)
## 2. No 127 (78.4) 28 (71.8) 155 (77.1)
write.csv(T4c, file="Table 4c.csv")
T5 <- tableStack(vars=c(A_Remiss, A_Relapsed, rel_BM, rel_CNS, rel_Testis, A_type_relapsed, Status, outcome),
by=hlk, total.column = TRUE); T5
## A_type_relapsed has zero count in at least one row
## Status has zero count in at least one row
## No Yes Total Test stat. P value
## Total 165 42 207
##
## A_Remiss Chisq. (3 df) = 3.76 0.289
## 1. Yes 90 (54.5) 19 (45.2) 109 (52.7)
## 2. No 40 (24.2) 13 (31.0) 53 (25.6)
## 3. Not access 29 (17.6) 6 (14.3) 35 (16.9)
## 4. Death before complete induction 6 (3.6) 4 (9.5) 10 (4.8)
## A_Relapsed Chisq. (1 df) = 0.01 0.915
## 1. Yes 51 (30.9) 12 (28.6) 63 (30.4)
## 2. No 114 (69.1) 30 (71.4) 144 (69.6)
## rel_BM Chisq. (1 df) = 0.03 0.857
## Yes 48 (29.1) 11 (26.2) 59 (28.5)
## No 117 (70.9) 31 (73.8) 148 (71.5)
## rel_CNS Fisher's exact test 0.604
## Yes 4 (2.4) 2 (4.8) 6 (2.9)
## No 161 (97.6) 40 (95.2) 201 (97.1)
## rel_Testis Fisher's exact test 1
## Yes 1 (0.6) 0 (0.0) 1 (0.5)
## No 164 (99.4) 42 (100.0) 206 (99.5)
## A_type_relapsed Fisher's exact test 1
## 1. T-cell 0 (0.0) 0 (0.0) 0 (0.0)
## 2. B-cell 0 (0.0) 0 (0.0) 0 (0.0)
## 3. Myeloid series 46 (100.0) 12 (100.0) 58 (100.0)
## 4. N/A 0 (0.0) 0 (0.0) 0 (0.0)
## Status Fisher's exact test 0.68
## 1. Alive on chemotherapy 0 (0.0) 0 (0.0) 0 (0.0)
## 2. Alive off chemotherapy 39 (23.6) 8 (19.0) 47 (22.7)
## 3. Alive with alternative treatment 0 (0.0) 0 (0.0) 0 (0.0)
## 4. Death 126 (76.4) 34 (81.0) 160 (77.3)
## outcome Chisq. (1 df) = 0.18 0.669
## Alive 39 (23.6) 8 (19.0) 47 (22.7)
## Dead 126 (76.4) 34 (81.0) 160 (77.3)
write.csv(T5, file="Table 5.csv")
#
#
detachAllData()
#
# Overall survival
#
count(data, outcome)
## # A tibble: 2 × 2
## outcome n
## <fct> <int>
## 1 Alive 323
## 2 Dead 367
os.fit <- survfit(Surv(stime, outcome=="Dead")~1, data=data)
os.fit
## Call: survfit(formula = Surv(stime, outcome == "Dead") ~ 1, data = data)
##
## n events median 0.95LCL 0.95UCL
## [1,] 690 367 6.72 4.3 13.7
# The median survival time for the whole group was 4.32 years (3.31 - 9.21).
summary(os.fit, times=c(5,10))
## Call: survfit(formula = Surv(stime, outcome == "Dead") ~ 1, data = data)
##
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 5 326 328 0.523 0.0191 0.486 0.561
## 10 241 26 0.477 0.0194 0.441 0.517
# The 5- and 10-year survival rates were 48.9% and 44.9%, respectively.
ggsurvplot(os.fit, data=data,
legend="none",
xlab="Time (years)",
xlim=c(0,20))
# Late deaths (1 case remains. Death 22.8 years after diagnosis)
filter(data, stime>20 & outcome=="Dead") %>%
select(HN, A_dx, rdate, sdate, stime, death_cause)
## # A tibble: 2 × 6
## HN A_dx rdate sdate stime death_cause
## <fct> <date> <date> <date> <dbl> <fct>
## 1 839791 1998-05-06 2003-12-15 2021-07-28 23.2 1. Cancer related
## 2 865930 1998-10-15 1999-09-29 2021-07-27 22.8 1. Cancer related
# Stratified by hlk
os.fit.hlk <- survfit(Surv(stime, outcome=="Dead")~hlk, data=data)
os.fit.hlk
## Call: survfit(formula = Surv(stime, outcome == "Dead") ~ hlk, data = data)
##
## n events median 0.95LCL 0.95UCL
## hlk=No 568 280 13.58 6.89 NA
## hlk=Yes 122 87 1.59 1.25 2.17
summary(os.fit.hlk, times=c(5,10))
## Call: survfit(formula = Surv(stime, outcome == "Dead") ~ hlk, data = data)
##
## hlk=No
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 5 293 244 0.568 0.0209 0.529 0.611
## 10 217 23 0.520 0.0214 0.479 0.563
##
## hlk=Yes
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 5 33 84 0.309 0.0421 0.236 0.403
## 10 24 3 0.280 0.0413 0.210 0.374
# Survival curves
ggsurvplot(os.fit.hlk, data=data,
legend.title="Hyperleukocytosis",
legend.labs=c('No','Yes'),
xlab="Time (years)",
xlim=c(0,20),
pval=TRUE)
#
# Event-free survival
#
efs.fit <- survfit(Surv(etime, event)~1, data=data)
efs.fit
## Call: survfit(formula = Surv(etime, event) ~ 1, data = data)
##
## n events median 0.95LCL 0.95UCL
## [1,] 690 396 3.62 2.84 5.11
summary(efs.fit, times=c(5,10))
## Call: survfit(formula = Surv(etime, event) ~ 1, data = data)
##
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 5 289 370 0.462 0.0191 0.426 0.501
## 10 215 17 0.432 0.0191 0.396 0.472
ggsurvplot(efs.fit, data=data,
legend="none",
xlab="Time (years)",
xlim=c(0,20))
# Stratify by hlk
efs.fit.hlk <- survfit(Surv(etime, event)~hlk, data=data)
efs.fit.hlk
## Call: survfit(formula = Surv(etime, event) ~ hlk, data = data)
##
## n events median 0.95LCL 0.95UCL
## hlk=No 568 307 4.98 3.696 11.25
## hlk=Yes 122 89 1.26 0.723 1.81
# Rates
summary(efs.fit.hlk, times=c(5,10))
## Call: survfit(formula = Surv(etime, event) ~ hlk, data = data)
##
## hlk=No
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 5 257 283 0.499 0.0211 0.460 0.543
## 10 191 15 0.468 0.0213 0.428 0.511
##
## hlk=Yes
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 5 32 87 0.286 0.0410 0.216 0.379
## 10 24 2 0.268 0.0403 0.200 0.360
ggsurvplot(efs.fit.hlk, data=data,
legend.title="Hyperleukocytefsis",
legend.labs=c('No','Yes'),
xlab="Time (years)",
xlim=c(0,20),
pval=TRUE)
# Change reference group for clinical presentation variables
data <- mutate(data,
Mediastinal_mass=relevel(Mediastinal_mass, ref="No"),
Fever=relevel(Fever, ref="No"),
Arthitis=relevel(Arthitis, ref="No"),
Xray=relevel(Xray, ref="No"),
hepato=relevel(hepato, ref="No"),
Splenomegaly=relevel(Splenomegaly, ref="No"),
lymphodenopathy=relevel(lymphodenopathy, ref="No"),
Exopthalmos=relevel(Exopthalmos, ref="No"))
# Compare outcomes between HLK within ALL and AML subgroups
data.ALL <- filter(data, Mor=="ALL")
data.AML <- filter(data, Mor=="AML")
#
# Overall survival
#
# OS among ALL subgroup
os.fit.all.hlk <- survfit(Surv(stime, outcome=="Dead")~hlk, data=data.ALL)
os.fit.all.hlk
## Call: survfit(formula = Surv(stime, outcome == "Dead") ~ hlk, data = data.ALL)
##
## n events median 0.95LCL 0.95UCL
## hlk=No 403 154 23.23 22.78 NA
## hlk=Yes 80 53 1.84 1.51 4.28
# Rates
summary(os.fit.all.hlk, times=c(5,10))
## Call: survfit(formula = Surv(stime, outcome == "Dead") ~ hlk, data = data.ALL)
##
## hlk=No
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 5 249 129 0.678 0.0234 0.633 0.725
## 10 184 19 0.621 0.0248 0.574 0.672
##
## hlk=Yes
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 5 26 50 0.372 0.0544 0.279 0.495
## 10 18 3 0.327 0.0536 0.237 0.451
ggsurvplot(os.fit.all.hlk, data=data.ALL,
legend.title="Hyperleukocytefsis",
legend.labs=c('No','Yes'),
xlab="Time (years)",
xlim=c(0,20),
pval=TRUE)
ggsave(file="Figures/HLK_Figure_ALL_OS.png", width=6, height=6)
# OS among AML subgroup
os.fit.aml.hlk <- survfit(Surv(stime, outcome=="Dead")~hlk, data=data.AML)
os.fit.aml.hlk
## Call: survfit(formula = Surv(stime, outcome == "Dead") ~ hlk, data = data.AML)
##
## n events median 0.95LCL 0.95UCL
## hlk=No 165 126 1.11 0.868 1.76
## hlk=Yes 42 34 0.83 0.331 2.03
# Rates
summary(os.fit.aml.hlk, times=c(5,10))
## Call: survfit(formula = Surv(stime, outcome == "Dead") ~ hlk, data = data.AML)
##
## hlk=No
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 5 44 115 0.302 0.0358 0.239 0.381
## 10 33 4 0.272 0.0353 0.211 0.351
##
## hlk=Yes
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 5 7 34 0.19 0.0606 0.102 0.355
## 10 6 0 0.19 0.0606 0.102 0.355
ggsurvplot(os.fit.aml.hlk, data=data.AML,
legend.title="Hyperleukocytefsis",
legend.labs=c('No','Yes'),
xlab="Time (years)",
xlim=c(0,20),
pval=TRUE,
pval.coord=c(0,0.1))
ggsave(file="Figures/HLK_Figure_AML_OS.png", width=6, height=6)
#
# Event-free survival
#
# EFS among ALL subgroup
efs.fit.all.hlk <- survfit(Surv(etime, event)~hlk, data=data.ALL)
efs.fit.all.hlk
## Call: survfit(formula = Surv(etime, event) ~ hlk, data = data.ALL)
##
## n events median 0.95LCL 0.95UCL
## hlk=No 403 180 NA 12.75 NA
## hlk=Yes 80 55 1.53 1.24 2.97
# Rates
summary(efs.fit.all.hlk, times=c(5,10))
## Call: survfit(formula = Surv(etime, event) ~ hlk, data = data.ALL)
##
## hlk=No
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 5 217 164 0.591 0.0246 0.545 0.641
## 10 160 13 0.552 0.0252 0.505 0.604
##
## hlk=Yes
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 5 25 53 0.337 0.0529 0.247 0.458
## 10 18 2 0.310 0.0520 0.223 0.430
ggsurvplot(efs.fit.all.hlk, data=data.ALL,
legend.title="Hyperleukocytefsis",
legend.labs=c('No','Yes'),
xlab="Time (years)",
xlim=c(0,20),
pval=TRUE)
ggsave(file="Figures/HLK_Figure_ALL_efs.png", width=6, height=6)
# efs among AML subgroup
efs.fit.aml.hlk <- survfit(Surv(etime, event)~hlk, data=data.AML)
efs.fit.aml.hlk
## Call: survfit(formula = Surv(etime, event) ~ hlk, data = data.AML)
##
## n events median 0.95LCL 0.95UCL
## hlk=No 165 127 0.827 0.665 1.36
## hlk=Yes 42 34 0.579 0.331 1.51
# Rates
summary(efs.fit.aml.hlk, times=c(5,10))
## Call: survfit(formula = Surv(etime, event) ~ hlk, data = data.AML)
##
## hlk=No
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 5 40 119 0.277 0.0350 0.216 0.355
## 10 31 2 0.262 0.0347 0.202 0.339
##
## hlk=Yes
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 5 7 34 0.19 0.0606 0.102 0.355
## 10 6 0 0.19 0.0606 0.102 0.355
ggsurvplot(efs.fit.aml.hlk, data=data.AML,
legend.title="Hyperleukocytefsis",
legend.labs=c('No','Yes'),
xlab="Time (years)",
xlim=c(0,20),
pval=TRUE,
pval.coord=c(0,0.1))
ggsave(file="Figures/HLK_Figure_AML_EFS.png", width=6, height=6)
# Risk factors associated with death
#
# Univariate analysis
#
# In cox regression analysis, could we divide age group in 2 group: <= 10 years(120 month) and > 10 years?
data <- mutate(data, agegp=cut(Age, br=c(0,120,Inf), labels=c("<=10",">10")))
count(data, agegp)
## # A tibble: 2 × 2
## agegp n
## <fct> <int>
## 1 <=10 555
## 2 >10 135
#
# Stratify by Morphology (ALL/AML)
#
# ALL subgroup
tbl_6a <-
tbl_uvregression(
data.ALL[c("hlk", "outcome", "agegp", "sex",
"Mediastinal_mass", "Fever", "Arthitis", "Xray",
"hepato", "Splenomegaly", "lymphodenopathy",
"Exopthalmos", "early_com", "stime")],
method = coxph,
y = Surv(stime, outcome=="Dead"),
exponentiate = TRUE,
pvalue_fun = function(x) style_pvalue(x, digits = 2)
)
t6a <- bold_labels(tbl_6a)
# Multivariate
cox1 <- coxph(Surv(stime, outcome=="Dead")~hlk+agegp+sex+
Mediastinal_mass+Fever+Arthitis+Xray+
hepato+Splenomegaly+lymphodenopathy+
Exopthalmos+early_com, data=data.ALL)
t6b <- tbl_regression(cox1, expo=TRUE) %>%
bold_labels()
tbl_merge(
tbls = list(t6a, t6b),
tab_spanner = c("**Univariate**", "**Multivariate**")) %>%
as_flex_table() %>%
save_as_html(
path="Table 6.html",
encoding = "utf-8")
# AML subgroup
tbl_9a <-
tbl_uvregression(
data.AML[c("hlk", "outcome", "agegp", "sex",
"Mediastinal_mass", "Fever", "Arthitis", "Xray",
"hepato", "Splenomegaly", "lymphodenopathy",
"Exopthalmos", "early_com", "stime")],
method = coxph,
y = Surv(stime, outcome=="Dead"),
exponentiate = TRUE,
pvalue_fun = function(x) style_pvalue(x, digits = 2)
)
t9a <- bold_labels(tbl_9a)
# Multivariate
cox2 <- coxph(Surv(stime, outcome=="Dead")~hlk+agegp+sex+
Mediastinal_mass+Fever+Arthitis+Xray+
hepato+Splenomegaly+lymphodenopathy+
Exopthalmos+early_com, data=data.AML)
t9b <- tbl_regression(cox2, expo=TRUE) %>%
bold_labels()
tbl_merge(
tbls = list(t9a, t9b),
tab_spanner = c("**Univariate**", "**Multivariate**")) %>%
as_flex_table() %>%
save_as_html(
path="Table 9.html",
encoding = "utf-8")
#
# Event-free survival
#
tbl_8a <-
tbl_uvregression(
data.ALL[c("hlk", "event", "agegp", "sex",
"Mediastinal_mass", "Fever", "Arthitis", "Xray",
"hepato", "Splenomegaly", "lymphodenopathy",
"Exopthalmos", "early_com", "etime")],
method = coxph,
y = Surv(etime, event),
exponentiate = TRUE,
pvalue_fun = function(x) style_pvalue(x, digits = 2)
)
t8a <- bold_labels(tbl_8a)
# Multivariate
cox1 <- coxph(Surv(etime, event)~hlk+agegp+sex+
Mediastinal_mass+Fever+Arthitis+Xray+
hepato+Splenomegaly+lymphodenopathy+
Exopthalmos+early_com, data=data.ALL)
t8b <- tbl_regression(cox1, expo=TRUE) %>%
bold_labels()
tbl_merge(
tbls = list(t8a, t8b),
tab_spanner = c("**Univariate**", "**Multivariate**")) %>%
as_flex_table() %>%
save_as_html(
path="Table 8.html",
encoding = "utf-8")
# AML subgroup
tbl_9a <-
tbl_uvregression(
data.AML[c("hlk", "event", "agegp", "sex",
"Mediastinal_mass", "Fever", "Arthitis", "Xray",
"hepato", "Splenomegaly", "lymphodenopathy",
"Exopthalmos", "early_com", "stime")],
method = coxph,
y = Surv(stime, event),
exponentiate = TRUE,
pvalue_fun = function(x) style_pvalue(x, digits = 2)
)
t9a <- bold_labels(tbl_9a)
# Multivariate
cox2 <- coxph(Surv(etime, event)~hlk+agegp+sex+
Mediastinal_mass+Fever+Arthitis+Xray+
hepato+Splenomegaly+lymphodenopathy+
Exopthalmos+early_com, data=data.AML)
t9b <- tbl_regression(cox2, expo=TRUE) %>%
bold_labels()
tbl_merge(
tbls = list(t9a, t9b),
tab_spanner = c("**Univariate**", "**Multivariate**")) %>%
as_flex_table() %>%
save_as_html(
path="Table 9.html",
encoding = "utf-8")