title: Estimation of the cumulative incidence function under multiple dependent and independent censoring mechanisms.
abstract: Competing risks occur in a time-to-event analysis in which a patient can experience one of several types of events. Traditional methods for handling competing risks data presuppose one censoring process, which is assumed to be independent. In a controlled clinical trial, censoring can occur for several reasons: some independent, others dependent. We propose an estimator of the cumulative incidence function in the presence of both independent and dependent censoring mechanisms. We rely on semi-parametric theory to derive an augmented inverse probability of censoring weighted (AIPCW) estimator. We demonstrate the efficiency gained when using the AIPCW estimator compared to a non-augmented estimator via simulations. We then apply our method to evaluate the safety and efficacy of three anti-HIV regimens in a randomized trial conducted by the AIDS Clinical Trial Group, ACTG A5095.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA. jlok@hsph.harvard.edu.
authors.forename: Judith J
authors.identifier: 0000-0001-7526-2572
authors.initials: JJ
authors.lastname: Lok
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, NC, USA.
authors.forename: Shu
authors.initials: S
authors.lastname: Yang
authors.affiliation: Incyte, Wilmington, DE, USA.
authors.forename: Brian
authors.initials: B
authors.lastname: Sharkey
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA.
authors.forename: Michael D
authors.initials: MD
authors.lastname: Hughes
mesh_terms: D000465:Algorithms; D015982:Bias; D004358:Drug Therapy; D015658:HIV Infections / Q000188:drug therapy; D015994:Incidence; D015233:Models, Statistical; D015337:Multicenter Studies as Topic; D016032:Randomized Controlled Trials as Topic; D012449:Safety
keywords: Competing risks; Cumulative incidence function; Dependent censoring; Inverse probability weighting