> Dear All,
> I am in some difficulty with predicting 'expected time of survival' for each
> observation for a glmnet cox family with LASSO.
> I have two dataset 50000 * 450 (obs * Var) and 8000 * 450 (obs * var), I
> considered first one as train and second one as test.
> I got the predict output and I am bit lost here,
> pre <- predict(fit,type="response", newx =selectedVar[1:20,])
> 1 0.9454985
> 2 0.6684135
> 3 0.5941740
> 4 0.5241938
> 5 0.5376783
> This is the output I am getting - I understood with type "response" gives
> the fitted relative-risk for "cox" family.
> I would like to know how I can convert it or change the fitted relative-risk
> to 'expected time of survival' ?
> Any help would be great, thanks for all your time and effort.
The answer is that you cannot predict survival time, in general. The reason is that most
studies do not follow the subjects for a sufficiently long time. For instance, say that
the data set comes from a study that enrolled subjects and then followed them for up to 5
years, at which time 35% had experienced mortality (using the usual Kaplan-Meier). Fit a
model to the data and ask "what is the predicted survival time for a low risk subject".
The answer will at best be "greater than 5 years". The program cannot say if it is 6 or
10 or even 1000. A bigger data set does not help.