Semiparametric analysis of interval‐censored failure time data with outcome‐dependent observation schemes

• Published in: Scandinavian journal of statistics Vol. 49; no. 1; pp. 236 - 264
• Main Authors: Zhu, Yayuan, Chen, Ziqi, Lawless, Jerald F.
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.03.2022
• Subjects: additive hazards model; Arthritis; Covariance matrix; dependent visit times; Estimators; Failure analysis; Failure times; Hazards; interval censoring; inverse‐intensity‐of‐visit weight; linear transformation models; Linear transformations; Regression coefficients; Regression models; semiparametric estimation
• ISSN: 0303-6898; 1467-9469
• DOI: 10.1111/sjos.12511

Disease progression is often monitored by intermittent follow‐up “visits” in longitudinal cohort studies, resulting in interval‐censored failure time outcomes. Furthermore, the timing and frequency of visits is often found related to a person's history of disease‐related variables in practice. This article develops a semiparametric estimation approach using weighted binomial regression and a kernel smoother to analyze interval‐censored failure time data. Visit times are allowed to be subject‐specific and outcome‐dependent. We consider a collection of widely used semiparametric regression models, including additive hazards and linear transformation models. For additive hazards models, the nonparametric component has a closed‐form estimator and the estimators of regression coefficients are shown to be asymptotically multivariate normal with sandwich‐type covariance matrices. Simulations are conducted to examine the finite sample performance of the proposed estimators. A data set from the Toronto Psoriatic Arthritis (PsA) Cohort Study is used to illustrate the proposed methodology.