Regression methods for gap time hazard functions of sequentially ordered multivariate failure time data

Abstract

Sequentially ordered multivariate failure time data are often observed in biomedical studies and inter‐event, or gap, times are often of interest. Generally, standard hazard regression methods cannot be applied to the gap times because of identifiability issues and induced dependent censoring. We propose estimating equations for fitting proportional hazards regression models to the gap times. Model parameters are shown to be consistent and asymptotically normal. Simulation studies reveal the appropriateness of the asymptotic approximations in finite samples. The proposed methods are applied to renal failure data to assess the association between demographic covariates and both time until wait‐listing and time from wait‐listing to kidney transplantation.