Estimation of the Cumulative Incidence Function Under Multiple Dependent and Independent Censoring Mechanisms

• Activity Number: 135 - Novel Non/Semiparametric Developments for Risk Perception with Censored and/or Missing Data
• Type: Invited
• Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
• Sponsor: Section on Risk Analysis
• Author(s): Judith Lok and Shu Yang* and Brian Sharkey and Michael Hughes
• Companies: Harvard T H Chan School of Public Health and North Carolina State University and NA and Harvard
• Keywords: Competing risks ; Cumulative incidence function ; Dependent censoring ; Inverse probability weighting

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 eciency gained when using the AIPCW estimator compared to a non-augmented estimator via simulations. We then apply our method to evaluate the safety and ecacy of two anti-HIV regimens in a randomized trial conducted by the AIDS Clinical Trial Group, ACTG A5095.