Semiparametric regression analysis of interval-censored competing risks data.

TitleSemiparametric regression analysis of interval-censored competing risks data.
Publication TypeJournal Article
Year of Publication2017
AuthorsMao, Lu, Dan-Yu Lin, and Donglin Zeng
JournalBiometrics
Volume73
Issue3
Pagination857-865
Date Published2017 09
ISSN1541-0420
KeywordsComputer Simulation, Likelihood Functions, Models, Statistical, Regression Analysis, Risk
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.

DOI10.1111/biom.12664
Alternate JournalBiometrics
Original PublicationSemiparametric regression analysis of interval-censored competing risks data.
PubMed ID28211951
PubMed Central IDPMC5561531
Grant ListR01 CA082659 / CA / NCI NIH HHS / United States
R37 AI029168 / AI / NIAID NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
P30 AI050410 / AI / NIAID NIH HHS / United States
R01 AI029168 / AI / NIAID NIH HHS / United States
R01 GM047845 / GM / NIGMS NIH HHS / United States