title: Semiparametric regression analysis of interval-censored competing risks 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 directly but rather is known to lie in an interval between two examinations. We formulate the effects of possibly time-varying (external) covariates on the cumulative incidence or sub-distribution function of competing risks (i.e., the marginal probability of failure from a specific cause) 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 for the regression parameters 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.
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53792, U.S.A.
authors.forename: Lu
authors.initials: L
authors.lastname: Mao
authors.affiliation: Department of Biostatistics, CB 7420, University of North Carolina, Chapel Hill, North Carolina 27599-7420, U.S.A.
authors.forename: Dan-Yu
authors.initials: DY
authors.lastname: Lin
authors.affiliation: Department of Biostatistics, CB 7420, University of North Carolina, Chapel Hill, North Carolina 27599-7420, U.S.A.
authors.forename: Donglin
authors.initials: D
authors.lastname: Zeng
mesh_terms: D003198:Computer Simulation; D016013:Likelihood Functions; D015233:Models, Statistical; D012044:Regression Analysis; D012306:Risk
keywords: Cumulative incidence; Interval censoring; Nonparametric maximum likelihood estimation; Self-consistency algorithm; Time-varying covariates; Transformation models