如何在R中的coxph函数中指定偏移项,稍后可在;mstate“;包裹

如何在R中的coxph函数中指定偏移项,稍后可在;mstate“;包裹,r,survival,R,Survival,我正在尝试使用R中的mstate包,为此,我必须使用strata命令使用coxph函数。下面是一个示例代码: library(mstate) tmat <- trans.illdeath() tg <- data.frame(stt=rep(0,6),sts=rep(0,6), illt=c(1,1,6,6,8,9),ills=c(1,0,1,1,0,1), dt=c(5,1,9,7,8,12),ds=c(1,1,1,1,1,1)) tg$patid <

我正在尝试使用R中的mstate包,为此,我必须使用strata命令使用coxph函数。下面是一个示例代码:

library(mstate)
tmat <- trans.illdeath()
tg <- data.frame(stt=rep(0,6),sts=rep(0,6), illt=c(1,1,6,6,8,9),ills=c(1,0,1,1,0,1),
             dt=c(5,1,9,7,8,12),ds=c(1,1,1,1,1,1))
tg$patid <- factor(2:7,levels=1:8,labels=as.character(1:8))
tt <- matrix(c(rep(NA,6),tg$illt,tg$dt),6,3)
st <- matrix(c(rep(NA,6),tg$ills,tg$ds),6,3)
mslong<-msprep(time=tt,status=st,trans=tmat)
models <- coxph(Surv(Tstart, Tstop, status) ~ strata(trans), data=mslong, method='breslow')
库(mstate)

tmat从技术上讲,您可以按如下方式进行:

# define c1 as an indicator for transition 3
mslong$c1<-ifelse(mslong$trans == 3, 1, 0)
model <-coxph(Surv(Tstart, Tstop, status) ~ strata(trans)+offset(2*c1), data=mslong, method='breslow')
偏移模型:

> coxph(Surv(Tstart, Tstop, status) ~ strata(trans)+offset(2*c1), data=mslong, method='breslow')
Call:  coxph(formula = Surv(Tstart, Tstop, status) ~ strata(trans) + 
    offset(2 * c1), data = mslong, method = "breslow")

Null model
  log likelihood= -7.742402 
  n= 16 

非常感谢@thc。您对我如何修改代码有何建议,以便我可以更改过渡=3的累积基线风险?
# define c1 as an indicator for transition 3
mslong$c1<-ifelse(mslong$trans == 3, 1, 0)
model <-coxph(Surv(Tstart, Tstop, status) ~ strata(trans)+offset(2*c1), data=mslong, method='breslow')
> coxph(Surv(Tstart, Tstop, status) ~ strata(trans), data=mslong, method='breslow')
Call:  coxph(formula = Surv(Tstart, Tstop, status) ~ strata(trans), 
    data = mslong, method = "breslow")

Null model
  log likelihood= -7.742402 
  n= 16 
> coxph(Surv(Tstart, Tstop, status) ~ strata(trans)+offset(2*c1), data=mslong, method='breslow')
Call:  coxph(formula = Surv(Tstart, Tstop, status) ~ strata(trans) + 
    offset(2 * c1), data = mslong, method = "breslow")

Null model
  log likelihood= -7.742402 
  n= 16