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All Times EDT

Friday, September 25
Fri, Sep 25, 3:30 PM - 4:45 PM
Virtual
Novel Survival Analysis When Hazards Are Nonproportional and/or There Are Multiple Types of Events

Estimation of the Cumulative Incidence Function Under Multiple Dependent and Independent Censoring Mechanisms (301219)

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*Judith Lok, Boston University Department of Mathematics and Statistics 

Keywords: Competing risks, Cumulative incidence function, Dependent censoring, Inverse probability weighting

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 semiparametric 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.