# Cox-Regression: Clustered Standard Errors?

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.

• I am not sure about clustered standard errors, but in the terminology for clustering in survival models that I am familiar with, multilevel 'clusters' are called 'frailties'. So, for example, if you have conducted a medical randomized placebo controlled trial in 50 different hospital and want to do a survival analysis, you could add hospital as a 'frailty' to your survival model, in order to incorporate a random effect. To conclude, if this is what you were looking for, you might want to look into frailties for survival models (coxph in R has this option) – IWS Nov 15 '17 at 13:13