library(readxl)
## Warning: package 'readxl' was built under R version 4.4.2
library(gtsummary)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(broom)
library(purrr)
library(gt)
# Đọc dữ liệu
outcomehq <- read_excel("C:/dl/data/R-Rstudio/outcomehq.xlsx",
col_types = c("text", "numeric", "numeric",
"text", "text", "numeric", "numeric",
"text", "text", "text", "text", "numeric",
"numeric", "numeric", "text", "text",
"numeric", "numeric", "numeric",
"text", "text", "text", "text", "text",
"text", "text", "numeric", "numeric",
"numeric", "numeric", "text", "text", "text", "text", "text",
"text", "text", "text", "text","numeric", "numeric", "text",
"text", "text", "numeric", "numeric", "text",
"numeric", "numeric", "text", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"text", "text", "text", "text", "text", "text", "text", "text", "numeric",
"text", "text", "text", "text", "text",
"text", "text", "text"))
# Chạy hồi quy logistic đơn biến cho từng biến độc lập
regression_results <- outcomehq %>%
select(age, sex, tha, dtd, dysl, htl, weight, height, bmi, classbmi,
cre, MDRD, CrCl, hr, HATTh, HATTr, GRACE, ClassGRACE, GRACE1, GRACE2, GRACE3,
ClassTIMI,TIMI1,TIMI2, STEMIequi, AtoD, AmitDSA, StoD, SymtompDSA,
cTnIhs1, cTnIhs2, Chototal, TG, HDLC, LDLC, leuko,
Hgb, dapt, LVEF, LVEF40, RWMA,
KILLIP1, Culprit, LAD, LCx, RCA, LM1, LM, Multivesseld, THBH,
RENTROP, PCIk, POBA, DES) %>%
imap(~ {
# Chạy hồi quy logistic cho từng biến
glm_formula <- as.formula(paste("MACCE ~", .y))
model <- glm(glm_formula, family = binomial, data = outcomehq)
# Lấy kết quả hồi quy
tidy(model, exponentiate = TRUE, conf.int = TRUE) %>%
mutate(variable = .y)
})
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
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## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
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## Warning: glm.fit: algorithm did not converge
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# Tính giá trị p chung cho các biến phân loại KILLIP và Culprit
categorical_vars <- c("KILLIP", "Culprit")
summary_table <- bind_rows(regression_results) %>%
filter(term != "(Intercept)") %>% # Loại bỏ hệ số chặn
select(variable, term, estimate, conf.low, conf.high, p.value) %>%
group_by(variable) %>%
summarise(
estimate = mean(estimate),
conf.low = mean(conf.low),
conf.high = mean(conf.high),
p.value = min(p.value) # Hoặc sử dụng max nếu muốn
) %>%
ungroup()
# Hiển thị kết quả với tên biến đầy đủ
summary_table %>%
gt::gt() %>%
tab_header(title = "Hồi quy logistic đơn biến cho MACCE",
subtitle = "Tỷ số chênh, khoảng tin cậy 95% và giá trị p")
| Hồi quy logistic đơn biến cho MACCE |
| Tỷ số chênh, khoảng tin cậy 95% và giá trị p |
| variable |
estimate |
conf.low |
conf.high |
p.value |
| AmitDSA |
4.400000e-01 |
1.636980e-02 |
1.180417e+01 |
0.57383900 |
| AtoD |
9.995707e-01 |
9.602757e-01 |
1.023764e+00 |
0.97678104 |
| Chototal |
1.362775e+00 |
5.525972e-01 |
3.856362e+00 |
0.50002270 |
| ClassGRACE |
7.121129e+07 |
NA |
NA |
0.99761037 |
| ClassTIMI |
2.328637e+07 |
4.256978e-250 |
NA |
0.99622427 |
| CrCl |
1.001682e+00 |
9.357672e-01 |
1.061199e+00 |
0.95411728 |
| Culprit |
1.798572e+18 |
0.000000e+00 |
NA |
0.99855382 |
| DES |
6.529063e-10 |
NA |
Inf |
0.99573456 |
| GRACE |
1.040900e+00 |
1.005194e+00 |
1.095090e+00 |
0.04513167 |
| GRACE1 |
4.453408e-08 |
NA |
1.250024e+265 |
0.99644783 |
| GRACE2 |
1.170226e-08 |
NA |
1.740249e+308 |
0.99671251 |
| GRACE3 |
1.424226e+08 |
5.403818e-249 |
NA |
0.99586105 |
| HATTh |
9.627859e-01 |
9.028809e-01 |
1.011107e+00 |
0.16261673 |
| HATTr |
9.540655e-01 |
8.627082e-01 |
1.036559e+00 |
0.29109015 |
| HDLC |
1.064552e-02 |
1.347534e-06 |
6.874579e+00 |
0.23215244 |
| Hgb |
1.165272e+00 |
5.688225e-01 |
2.867604e+00 |
0.70407211 |
| KILLIP1 |
2.322868e+09 |
0.000000e+00 |
NA |
0.99656771 |
| LAD |
4.453408e-08 |
NA |
1.250024e+265 |
0.99644783 |
| LCx |
1.117647e+00 |
4.199724e-02 |
2.974912e+01 |
0.93898372 |
| LDLC |
1.837851e+00 |
4.955768e-01 |
9.671430e+00 |
0.38576970 |
| LM |
1.700000e+01 |
5.427451e-01 |
5.720842e+02 |
0.07484136 |
| LM1 |
1.531613e+09 |
0.000000e+00 |
NA |
0.99573456 |
| LVEF |
9.984415e-01 |
8.875488e-01 |
1.129741e+00 |
0.97830187 |
| LVEF40 |
3.976257e-08 |
NA |
3.824614e+224 |
0.99580683 |
| MDRD |
1.006781e+00 |
9.131549e-01 |
1.100795e+00 |
0.87924022 |
| Multivesseld |
9.090909e-02 |
2.990677e-03 |
2.679446e+00 |
0.11883143 |
| PCIk |
5.882353e-02 |
1.747994e-03 |
1.842486e+00 |
0.07484136 |
| POBA |
1.340265e-07 |
NA |
2.792863e+202 |
0.99567138 |
| RCA |
3.976257e-08 |
NA |
3.824614e+224 |
0.99580683 |
| RENTROP |
2.000000e+00 |
NA |
Inf |
0.35088486 |
| RWMA |
5.652174e-01 |
2.113782e-02 |
1.509634e+01 |
0.69519510 |
| STEMIequi |
5.000000e+00 |
1.810784e-01 |
1.391200e+02 |
0.27788477 |
| StoD |
9.964810e-01 |
9.606917e-01 |
1.020515e+00 |
0.80003359 |
| SymtompDSA |
3.846154e-01 |
1.425606e-02 |
1.035135e+01 |
0.51349254 |
| TG |
9.317570e-01 |
2.379751e-01 |
1.046057e+00 |
0.83331083 |
| THBH |
2.000000e+00 |
7.473643e-02 |
5.360107e+01 |
0.63443415 |
| TIMI1 |
4.294357e-08 |
NA |
2.349084e+249 |
0.99622427 |
| TIMI2 |
2.328637e+07 |
4.256978e-250 |
NA |
0.99622427 |
| age |
9.699761e-01 |
8.537558e-01 |
1.096926e+00 |
0.60693079 |
| bmi |
8.919923e-01 |
4.324308e-01 |
1.335845e+00 |
0.66801121 |
| cTnIhs1 |
1.000038e+00 |
9.996107e-01 |
1.000257e+00 |
0.77866455 |
| cTnIhs2 |
1.000032e+00 |
1.000006e+00 |
1.000078e+00 |
0.04838422 |
| classbmi |
2.272727e+00 |
8.471581e-02 |
6.108811e+01 |
0.57383900 |
| cre |
1.044858e+00 |
9.424219e-01 |
1.202521e+00 |
0.44185266 |
| dapt |
9.812661e-01 |
8.142883e-01 |
1.165586e+00 |
0.82129200 |
| dtd |
1.170226e-08 |
NA |
1.740249e+308 |
0.99671251 |
| dysl |
8.947368e-01 |
3.361444e-02 |
2.381109e+01 |
0.93898372 |
| height |
2.002833e-01 |
9.086594e-11 |
6.684696e+07 |
0.86990911 |
| hr |
9.536153e-01 |
8.734706e-01 |
1.022868e+00 |
0.20866137 |
| htl |
3.500000e+00 |
1.288339e-01 |
9.547445e+01 |
0.39407174 |
| leuko |
1.277693e+00 |
8.649052e-01 |
1.941498e+00 |
0.18972113 |
| sex |
1.170226e-08 |
NA |
1.740249e+308 |
0.99671251 |
| tha |
1.612903e-01 |
5.701245e-03 |
4.512246e+00 |
0.22201365 |
| weight |
9.769109e-01 |
8.357937e-01 |
1.089458e+00 |
0.71507516 |
# Hàm để thay thế giá trị thiếu bằng số trung bình
replace_na_numeric <- function(df, vars) {
for (var in vars) {
if (var %in% colnames(df)) {
mean_value <- mean(df[[var]], na.rm = TRUE)
df[[var]][is.na(df[[var]])] <- mean_value
}
}
return(df)
}
# Hàm để thay thế giá trị thiếu bằng mode
replace_na_factor <- function(df, vars) {
for (var in vars) {
if (var %in% colnames(df)) {
mode_value <- names(sort(table(df[[var]]), decreasing = TRUE))[1]
df[[var]][is.na(df[[var]])] <- mode_value
}
}
return(df)
}
# Thay thế giá trị thiếu cho các biến định lượng
outcomehq <- replace_na_numeric(outcomehq, c("cTnIhs2", "dapt", "HDLC"))
# Thay thế giá trị thiếu cho các biến định tính
outcomehq <- replace_na_factor(outcomehq, c("THBH", "RENTROP"))
# Tạo ma trận thiết kế
xvars <- model.matrix(MACCE ~ cTnIhs2 + Multivesseld , data = outcomehq)[,-1] # Loại bỏ cột intercept
yvar <- outcomehq$MACCE
library(glmnet)
## Loading required package: Matrix
## Loaded glmnet 4.1-8
# Hồi quy Ridge và Lasso
ridge <- cv.glmnet(xvars, yvar, alpha = 0)
plot(ridge)
coef(ridge)
## 3 x 1 sparse Matrix of class "dgCMatrix"
## s1
## (Intercept) 5.263158e-02
## cTnIhs2 3.984911e-42
## Multivesseld1 -2.228164e-37
lasso <- cv.glmnet(xvars, yvar, alpha = 1)
plot(lasso)
coef(lasso)
## 3 x 1 sparse Matrix of class "dgCMatrix"
## s1
## (Intercept) 0.05263158
## cTnIhs2 .
## Multivesseld1 .
# Kết quả LASSO cho thấy có các biến đều có hiện tượng đa cộng tuyến ## Đưa các biến Multivesseld và cTnThs2 vào chạy đa biến
temp1 <- glm(MACCE ~ cTnIhs2 + Multivesseld, family="binomial", data=outcomehq)
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
search1 <- step(temp1)
## Start: AIC=9.02
## MACCE ~ cTnIhs2 + Multivesseld
##
## Df Deviance AIC
## <none> 3.0158 9.0158
## - Multivesseld 1 10.0656 14.0656
## - cTnIhs2 1 13.5217 17.5217
summary(search1)
##
## Call:
## glm(formula = MACCE ~ cTnIhs2 + Multivesseld, family = "binomial",
## data = outcomehq)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.087e+00 9.622e+00 -0.633 0.527
## cTnIhs2 4.269e-04 6.945e-04 0.615 0.539
## Multivesseld1 -5.247e+01 5.461e+03 -0.010 0.992
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 15.6706 on 37 degrees of freedom
## Residual deviance: 3.0158 on 35 degrees of freedom
## AIC: 9.0158
##
## Number of Fisher Scoring iterations: 21
# Sử dụng hàm step để chọn biến
search <- step(temp1)
## Start: AIC=9.02
## MACCE ~ cTnIhs2 + Multivesseld
##
## Df Deviance AIC
## <none> 3.0158 9.0158
## - Multivesseld 1 10.0656 14.0656
## - cTnIhs2 1 13.5217 17.5217
# Tính OR và khoảng tin cậy 95%
OR <- exp(coef(temp1))
conf_int <- exp(confint(temp1))
## Waiting for profiling to be done...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
results <- cbind(OR, conf_int)
colnames(results) <- c("OR", "2.5%", "97.5%")
results
## OR 2.5% 97.5%
## (Intercept) 2.271200e-03 NA 4.82112e-01
## cTnIhs2 1.000427e+00 1.000003e+00 NA
## Multivesseld1 1.629589e-23 6.220098e-303 4.26932e+256