#竞争风险模型
tmp <- data.frame(gene = tiaoxuan[,5:6],
OS.Time = Train[,"Survival_months"],
OS = Train[,"CSS"],
stringsAsFactors = F)
colnames(tmp)
#方法1：riskregression
library(riskRegression)
fgr1<-FGR(Hist(OS.Time,OS)~gene.Cancer_directed_surgery+gene.Systemic_therapy,data = tmp,cause = 1)
#方法2：cmprsk
library(cmprsk)
help(package="cmprsk")
CIF <- cuminc(ftime = tmp$OS.Time, fstatus =tmp$OS, group = tmp$gene.Cancer_directed_surgery, cencode = 0)
CIF
vars <- tmp[, c("gene.Cancer_directed_surgery","gene.Systemic_therapy")] %>% sapply(.,as.numeric)
str(tmp)
fit <- crr(ftime=tmp$OS.Time, fstatus=tmp$OS, vars,failcode=1,cencode = 0) ##拟合模型
summary(fit)
#mstate：构建列线图
library(mstate)
tmp$id<-1:1193
df.w <- crprep("OS.Time", "OS",
data=tmp, trans=c(1,2),
id="id",
cens=0,keep=c("gene.Cancer_directed_surgery","gene.Systemic_therapy"))
df.w$Time<- df.w$Tstop - df.w$Tstart
cox <- coxph(Surv(Time,status==1)~ gene.Cancer_directed_surgery+gene.Systemic_therapy, data=df.w[df.w$failcode==1,],
weight=weight.cens,
subset=failcode==1)
cox
library(regplot)
regplot(cox)
值得注意的是，使用crprep构建模型的系数与上述两种方法相近但是不一致，知道原因的同志欢迎在评论区留言。
