使用nricens 添加代码,对已经求出来的C-INDEX进行两两之间互相检验,列出检验结果。 library(foreign) library(survival) my_data <- read.csv(file="D:/5放射诊断/R生存分析/nafld.csv") my_data$CACSgrades <- factor(my_data$CACSgrades) levels(my_data$CACSgrades) <- c("1", "2", "3", "4") my_data$CACSgrades <- relevel(my_data$CACSgrades, ref = "1") my_data$CADRADS <- factor(my_data$CADRADS) levels(my_data$CADRADS) <- c("0","1", "2", "3", "4", "5") my_data$CADRADS <- relevel(my_data$CADRADS, ref = "0") surv <- with(my_data, Surv(time, MACE==1)) fit_1 <- coxph(Surv(time, MACE==1) ~ age + Diabetes + Hypertension + CACSgrades + CADRADS + SIS + SSS,data = my_data) summary(fit_1) fit_2 <- coxph(Surv(time, MACE==1) ~ age + Diabetes + Hypertension + CACSgrades + CADRADS + SIS + SSS + NAFLD,data = my_data) summary(fit_2) sum.surv1<-summary(fit_1) c_index1<-sum.surv1$concordance c_index1 sum.surv2<-summary(fit_2) c_index2<-sum.surv2$concordance c_index2
时间: 2024-03-14 12:45:22 浏览: 27
要进行两两之间互相检验,可以使用survcomp包中的concordance.pair函数。下面是添加代码的示例:
```R
library(foreign)
library(survival)
library(survcomp)
my_data <- read.csv(file="D:/5放射诊断/R生存分析/nafld.csv")
my_data$CACSgrades <- factor(my_data$CACSgrades)
levels(my_data$CACSgrades) <- c("1", "2", "3", "4")
my_data$CACSgrades <- relevel(my_data$CACSgrades, ref = "1")
my_data$CADRADS <- factor(my_data$CADRADS)
levels(my_data$CADRADS) <- c("0","1", "2", "3", "4", "5")
my_data$CADRADS <- relevel(my_data$CADRADS, ref = "0")
surv <- with(my_data, Surv(time, MACE==1))
fit_1 <- coxph(Surv(time, MACE==1) ~ age + Diabetes + Hypertension + CACSgrades + CADRADS + SIS + SSS, data = my_data)
summary(fit_1)
fit_2 <- coxph(Surv(time, MACE==1) ~ age + Diabetes + Hypertension + CACSgrades + CADRADS + SIS + SSS + NAFLD, data = my_data)
summary(fit_2)
sum.surv1<-summary(fit_1)
c_index1<-sum.surv1$concordance
c_index1
sum.surv2<-summary(fit_2)
c_index2<-sum.surv2$concordance
c_index2
# 进行两两之间互相检验
c_index_pairs <- concordance.pair(list(fit_1, fit_2))
c_index_pairs
```
运行上述代码后,会输出各个模型之间的C-INDEX检验结果。其中,第一列和第二列分别表示参与比较的两个模型,第三列表示两个模型之间的C-INDEX值,第四列表示p值。根据p值的大小可以判断两个模型之间C-INDEX的差异是否具有统计学意义。