[英]Cox proportional hazard model-interaction
I want to test for an interaction ( for Cox proportional hazard model) between type of transplant and disease type using main effects and interaction terms on the data bone marrow transplant study at Ohio State University.我想在俄亥俄州立大学的数据骨髓移植研究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)。
Here is the used code for the data:这是数据的使用代码:
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))
Here is the code for interaction, but I'm not sure what it should be written in here (myData$(here?) from the following code to be able to run it.这是用于交互的代码,但我不确定应该从以下代码中将其写入此处 (myData$(here?) 以便能够运行它。
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)
An easy way is to separate the four groups variables using the separate
function from the tidyr package.一种简单的方法是使用tidyr包中的separate
函数将四个组变量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
Then you can put these two new variables ( disease
and transpl
) into the Cox model, with interaction term.然后你可以将这两个新变量( disease
和transpl
)放入 Cox 模型中,并带有交互项。
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
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