Cox 比例風險模型-相互作用

Question

我想在俄亥俄州立大學的數據骨髓移植研究中使用主效應和交互項來測試移植類型和疾病類型之間的交互作用（對於 Cox 比例風險模型）。

這是數據的使用代碼：

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))

這是用於交互的代碼，但我不確定應該從以下代碼中將其寫入此處 (myData$(here?) 以便能夠運行它。

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

一種簡單的方法是使用tidyr包中的separate函數將四個組變量separate 。

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

然后你可以將這兩個新變量（ disease和transpl ）放入 Cox 模型中，並帶有交互項。

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