Nonparametric analysis of dependently interval‐censored failure time data

Abstract

Failure time studies based on observational cohorts often have to deal with irregular intermittent observation of individuals, which produces interval‐censored failure times. When the observation times depend on factors related to a person's failure time, the failure times may be dependently interval censored. Inverse‐intensity‐of‐visit weighting methods have been developed for irregularly observed longitudinal or repeated measures data and recently extended to parametric failure time analysis. This article develops nonparametric estimation of failure time distributions using weighted generalized estimating equations and monotone smoothing techniques. Simulations are conducted for examination of the finite sample performance of proposed estimators. This research is motivated in part by the Toronto Psoriatic Arthritis Cohort Study, and the proposed methodology is applied to this study.