Semiparametric Regression Analysis of Interval-Censored Competing Risks Data

TL;DR

This paper develops a semiparametric regression framework for interval-censored competing risks data, allowing for flexible modeling of covariate effects on cause-specific failure probabilities, with proven statistical properties and practical algorithms.

Contribution

It introduces a broad class of semiparametric models for interval-censored competing risks data, including efficient estimation methods and theoretical guarantees.

Findings

Establishes consistency and asymptotic normality of estimators.

Demonstrates good performance through simulation studies.

Applies methods to HIV-1 infection data.

Abstract

Interval-censored competing risks data arise when each study subject may experience an event or failure from one of several causes and the failure time is not observed exactly but rather known to lie in an interval between two successive examinations. We formulate the effects of possibly time-varying covariates on the cumulative incidence or sub-distribution function (i.e., the marginal probability of failure from a particular cause) of competing risks through a broad class of semiparametric regression models that captures both proportional and non-proportional hazards structures for the sub-distribution. We allow each subject to have an arbitrary number of examinations and accommodate missing information on the cause of failure. We consider nonparametric maximum likelihood estimation and devise a fast and stable EM-type algorithm for its computation. We then establish the consistency, asymptotic normality, and semiparametric efficiency of the resulting estimators by appealing to modern empirical process theory. In addition, we show through extensive simulation studies that the proposed methods perform well in realistic situations. Finally, we provide an application to a study on HIV-1 infection with different viral subtypes.