Dear All,
I have built a survival cox-model, which includes a covariate * time
interaction. (non-proportionality detected)
I am now wondering how could I most easily get survival predictions from my
model.
My model was specified:
coxph(formula = Surv(event_time_mod, event_indicator_mod) ~ Sex +
ageC + HHcat_alt + Main_Branch + Acute_seizure + TreatmentType_binary +
ICH + IVH_dummy + IVH_dummy:log(event_time_mod)
And now I was hoping to get a prediction using survfit and providing new.data
for the combination of variables
I am doing the predictions:
survfit(cox, new.data=new)
Now as I have event_time_mod in the right-hand side in my model I need to
specify it in the new data frame passed on
to survfit. This event_time would need to be set at individual times of the
predictions. Is there an easy way to specify event_time_mod to be the correct
time to survfit?
Or are there any other options for achieving predictions from my model?
Of course I could create as many rows in the new data frame as there are
distinct times in the predictions and setting to event_time_mod to correct
values but it feels really cumbersome and
I thought that there must be a better way.
Best,
Mitja
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