I'm using a coxph-model (in R) to model survival time on prepayment loans. My data consists of monthly panel (longnitude) data o.v.t. to adress time-varying covariates, f.e.: loan ID 1 last for 12 months, so I got 12 rows, loan ID 2 lasts for 3,5 years, so I have 30 rows etc.
Having already a suitable standard model cox model, I'm wondering if clustered standard issues could be an serious "threat".
In usual regression, clustered standard errors are the usual way to go. Specifically, in my case, due to the panel structure the i.i.d assumption does not hold.
As far as I understood, the standard errors which is provided by the
coxph() regression in R only delivers homoscedactic standard errors.
Did I get this right so far? If yes, is there any possibility to get heteroscedastic ("Sandwich") Standard Errors. I read that the
cluster() option might be an option, but I'm not sure if actually adresses heteroscedacity.
Note that I'm not talking about unobserved heteroscedacity, I'm concerned with the heteroscedacity of the standard errors.