Dear R-help Community,
I'm currently struggling with some issues extending the proportional Cox
model with time-dependent coefficients and could really need some help.
Since I'm not experienced in adding code in an email in a nice way I add the
link to my question and code:
https://stats.stackexchange.com/questions/297052/time-dependend-coefficients-in-a-cox-model
Essentially, I try to predict the time to default in a credit scoring context
using six categorical variables with survival analysis.
To extend the basic proportional Cox model I introduce time-dependent
coefficients using a step function beta(t) according to Therneau et al.
"Using time depended covariates and time depended coefficients in the Cox
model".
First question is: how to get the overall test for each categorical variable
after applying the Cox.zph function.
The second, and currently more important question for me is:
After fitting my model based on a training set is it possible to make a
prediction based on a new testset? Since I use an interaction between the
covariate BS24 and the stratification by time group on the right handside of my
formula (see code in link), I'm a bit confused how prediction is going to
work. At the initial time t=0 I have no idea of the future particularly in which
time group the new customer will be.
To compare my models I use the time depended AUC calculated by the
"score" function from package riskRegression. It internally uses a
prediction function but when applying it to the extended model and the new test
set it says "strata(time group) not found".
When splitting my test set with survSplit the same way I did for the training
set, it works and I get a nice AUC plot. But I am afraid if this actually makes
sense and I don't wrongly include future information I usually don't
have.
I'm thankful for any help or hint.
Kind regards
Theresa
