Hi everybody,

I'm fairly new to survival analysis with R and have some questions how to
apply and interpret the coxph and related functions:

I have time-dependent covariates with several measurements per subject with
constant delta t. The covariates change in each time step.

I fitted the following model:

fit <- coxph(Surv(start, stop, event) ~ ratePO + rateC + BLamp + BLP80 +
cluster(subjectID), data=dat)

and get

n= 1081, number of events= 10

             coef        exp(coef) se(coef)   robust se  z
Pr(>|z|)
ratePO  -0.50189   0.60539  0.25195   0.17696   -2.836  0.004565 **
BLamp  -0.05340   0.94800  0.02877   0.01470   -3.632  0.000281 ***
rateC     0.82888   2.29076  0.38111   0.30110     2.753  0.005908 **
BLP80   0.19425   1.21440  0.11377   0.04320     4.497  6.9e-06 ***

Rsquare= 0.035   (max possible= 0.056 )
Likelihood ratio test= 38.62  on 4 df,   p=8.362e-08
Wald test            = 46.71  on 4 df,   p=1.756e-09
Score (logrank) test = 31.03  on 4 df,   p=3.022e-06,   Robust = 13.91
p=0.007582

Now for a single subject I want to use this model in the sense of a
classifier to predict the event probability at time t.
Do I have to use the function predict.coxph? Which of the types is the right
one? Or can I also use survfit for this purpose?

I know these are pretty basic questions, but I'm kind of lost here.
Any comments would be of great help.

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