Semiparametric regression analysis of partly interval‐censored failure time data with application to an AIDS clinical trial

• Published in: Statistics in medicine Vol. 40; no. 20; pp. 4376 - 4394
• Main Authors: Zhou, Qingning, Sun, Yanqing, Gilbert, Peter B.
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 10.09.2021
• Subjects: Acquired Immunodeficiency Syndrome - drug therapy; Acquired Immunodeficiency Syndrome - epidemiology; AIDS clinical trial; Clinical trials; Computer Simulation; EM algorithm; Failure analysis; Humans; Likelihood Functions; partly interval‐censored data; Proportional Hazards Models; Regression Analysis; semiparametric transformation models; survival analysis; partly interval-censored data; semiparametric transformation models; survival analysis; EM algorithm; AIDS clinical trial
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.9035

Failure time data subject to various types of censoring commonly arise in epidemiological and biomedical studies. Motivated by an AIDS clinical trial, we consider regression analysis of failure time data that include exact and left‐, interval‐, and/or right‐censored observations, which are often referred to as partly interval‐censored failure time data. We study the effects of potentially time‐dependent covariates on partly interval‐censored failure time via a class of semiparametric transformation models that includes the widely used proportional hazards model and the proportional odds model as special cases. We propose an EM algorithm for the nonparametric maximum likelihood estimation and show that it unifies some existing approaches developed for traditional right‐censored data or purely interval‐censored data. In particular, the proposed method reduces to the partial likelihood approach in the case of right‐censored data under the proportional hazards model. We establish that the resulting estimator is consistent and asymptotically normal. In addition, we investigate the proposed method via simulation studies and apply it to the motivating AIDS clinical trial.