I encountered a case where the coxph model result is exactly the same with and without time dependent covariate adjustment. I tested using the Stanford heart transplant dataset (jasa) as exampled in Therneau et al 2019 and it also showed no difference. I wonder if I'm doing something wrong or it's data-specific (I do get result difference in other datasets).
time dependent model
library(survival)
jasa$subject <- 1:nrow(jasa)
tdata <- with(jasa, data.frame(subject = subject,
futime= pmax(.5, fu.date - accept.dt),
txtime= ifelse(tx.date== fu.date,
(tx.date -accept.dt) -.5,
(tx.date - accept.dt)),
fustat = fustat))
sdata <- tmerge(jasa, tdata, id=subject,
death = event(futime, fustat),
trt = tdc(txtime),
options= list(idname="subject"))
coxph(Surv(tstart, tstop, death) ~ surgery, data= sdata, ties="breslow")
Result:
Call:
coxph(formula = as.formula(form), data = sdata, ties = "breslow")
coef exp(coef) se(coef) z p
surgery -0.7391 0.4775 0.3591 -2.058 0.0396
Likelihood ratio test=5.05 on 1 df, p=0.02461
n= 170, number of events= 75
unadjusted
jasa <- jasa %>% mutate(futime = pmax(.5, fu.date - accept.dt))
coxph(Surv(futime, fustat) ~ surgery, data= jasa, ties="breslow")
Result:
Call:
coxph(formula = as.formula(form), data = jasa, ties = "breslow")
coef exp(coef) se(coef) z p
surgery -0.7391 0.4775 0.3591 -2.058 0.0396
Likelihood ratio test=5.05 on 1 df, p=0.02461
n= 103, number of events= 75
The results are exactly the same. I noted that the number of events are the same in both cases. The difference I expect is that the time dependent adjustment models in the immortal time to transplant and censors them. And since there is an uneven distribution of transplant in the two groups of patients with or without prior surgery, I do expect some difference with the unadjusted model. Is there something wrong with my thinking or the codes?