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

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.