Cox比例风险模型-交互

Question

我想在俄亥俄州立大学的骨髓移植研究数据中使用主效应和交互项来测试移植类型和疾病类型之间的交互作用（对于 Cox 比例风险模型）。

这里是数据使用的代码：

time_Allo_NHL<- c(28,32,49,84,357,933,1078,1183,1560,2114,2144)
censor_Allo_NHL<- c(rep(1,5), rep(0,6))
df_Allo_NHL <- data.frame(group = "Allo NHL", 
                          time = time_Allo_NHL,
                          censor = censor_Allo_NHL,
                          Z1 = c(90,30,40,60,70,90,100,90,80,80,90),
                          Z2 = c(24,7,8,10,42,9,16,16,20,27,5))

time_Auto_NHL<- c(42,53,57,63,81,140,176,210,252,476,524,1037)
censor_Auto_NHL<- c(rep(1,7), rep(0,1), rep(1,1), rep(0,1), rep(1,1), rep(0,1))
df_Auto_NHL <- data.frame(group = "Auto NHL", 
                          time = time_Auto_NHL, 
                          censor = censor_Auto_NHL,
                          Z1 = c(80,90,30,60,50,100,80,90,90,90,90,90),
                          Z2 = c(19,17,9,13,12,11,38,16,21,24,39,84))

time_Allo_HOD<- c(2,4,72,77,79)
censor_Allo_HOD<- c(rep(1,5))
df_Allo_HOD <- data.frame(group = "Allo HOD", 
                          time = time_Allo_HOD, 
                          censor = censor_Allo_HOD,
                          Z1 = c(20,50,80,60,70),
                          Z2 = c(34,28,59,102,71))

time_Auto_HOD<- c(30,36,41,52,62,108,132,180,307,406,446,484,748,1290,1345)
censor_Auto_HOD<- c(rep(1,7), rep(0,8))
df_Auto_HOD <- data.frame(group = "Auto HOD", 
                          time = time_Auto_HOD, 
                          censor = censor_Auto_HOD,
                          Z1 = c(90,80,70,60,90,70,60,100,100,100,100,90,90,90,80),
                          Z2 = c(73,61,34,18,40,65,17,61,24,48,52,84,171,20,98))

myData <- Reduce(rbind, list(df_Allo_NHL, df_Auto_NHL, df_Allo_HOD, df_Auto_HOD))

这是交互的代码，但是我不确定应该在这里写什么（myData$（这里？）来自下面的代码才能运行它。

n<-length(myData$time)
n

for (i in 1:n){
  if (myData$(here?)[i]==2)
    myData$W1[i] <-1
  else myData$W1[i]<-0
}

for (i in 1:n){
  if (myData$(here?)[i]==2)
    myData$W2[i] <-1
  else myData$W2[i]<-0
}

myData

Coxfit.W<-coxph(Surv(time,censor)~W1+W2+W1*W2, data = myData)
summary(Coxfit.W)

Answer 1

一个简单的方法是使用 separate 函数从 tidyr 包中分离四个组变量。

library(tidyr)

myData <- separate(myData, col=group, into=c("disease","transpl"))
head(myData)
  disease transpl time censor Z1 Z2
1    Allo     NHL   28      1 90 24
2    Allo     NHL   32      1 30  7
3    Allo     NHL   49      1 40  8
4    Allo     NHL   84      1 60 10
5    Allo     NHL  357      1 70 42
6    Allo     NHL  933      0 90  9

然后你可以把这两个新变量（disease和transpl）带入Cox模型，有交互项。

Coxfit.W<-coxph(Surv(time,censor)~transpl*disease, data = myData)
summary(Coxfit.W)

Call:
coxph(formula = Surv(time, censor) ~ transpl * disease, data = myData)

  n= 43, number of events= 26 

                          coef exp(coef) se(coef)      z Pr(>|z|)   
transplNHL             -1.8212    0.1618   0.6747 -2.699  0.00695 **
diseaseAuto            -1.6628    0.1896   0.6188 -2.687  0.00721 **
transplNHL:diseaseAuto  2.3050   10.0244   0.8494  2.714  0.00665 **

                       exp(coef) exp(-coef) lower .95 upper .95
transplNHL                0.1618    6.17946   0.04312    0.6073
diseaseAuto               0.1896    5.27387   0.05638    0.6377
transplNHL:diseaseAuto   10.0244    0.09976   1.89700   52.9720

Cox比例风险模型-交互

Cox proportional hazard model-interaction

r

cox-regression