Event-weighted proportional hazards modelling for recurrent gap time data.

Abstract

The analysis of gap times in recurrent events requires an adjustment to standard marginal models. One can perform this adjustment with a modified within-cluster resampling technique; however, this method is computationally intensive. In this paper, we describe a simple adjustment to the standard Cox proportional hazards model analysis that mimics the intent of within-cluster resampling and results in similar parameter estimates. This method essentially weights the partial likelihood contributions by the inverse of the number of gap times observed within the individual while assuming a working independence correlation matrix. We provide an example involving recurrent mammary tumours in female rats to illustrate the methods considered in this paper.