Estimation of the cumulative incidence function under multiple dependent and independent censoring mechanisms

Judith J. Lok (), Shu Yang (), Brian Sharkey () and Michael D. Hughes ()
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Judith J. Lok: Harvard School of Public Health
Shu Yang: North Carolina State University
Brian Sharkey: Incyte
Michael D. Hughes: Harvard School of Public Health

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2018, vol. 24, issue 2, No 1, 223 pages

Abstract: 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.

Keywords: Competing risks; Cumulative incidence function; Dependent censoring; Inverse probability weighting (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10985-017-9393-4

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