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Activity Number: 409 - Survival Analysis I
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #327096
Title: General Regression Model for the Subdistribution of a Competing Risk Under Left-Truncation and Right-Censoring
Author(s): Anna Bellach* and Michael Kosorok and Peter Gilbert and Jason P Fine
Companies: Fred Hutch Cancer Research Center and University of North Carolina at Chapel Hill and Fred Hutchinson Cancer Research Center and University of North Carolina at Chapel Hill
Keywords: Fine-Gray model; competing risks; left-truncation and right-censoring; HIV vaccine efficacy trials; semiparametric transformation models; time-varying covariates

Left-truncation poses additional challenges for the analysis of complex time to event data. We propose a general semiparametric regression model for left-truncated and right-censored competing risks data. Targeting the subdistribution hazard, our parameter estimates are directly interpretable with regard to the cumulative incidence function. Our approach accommodates external time dependent covariate effects on the subdistribution hazard. We establish consistency and asymptotic normality of the estimators and propose a sandwich estimator of the variance. In comprehensive simulation studies we demonstrate a solid performance of the proposed method, thereby comparing the sandwich estimator to the inverse Fisher information. Applying the new method to HIV-1 vaccine efficacy trial data we investigate how participant factors associate with the time from adulthood until HIV-1 infection.

Authors who are presenting talks have a * after their name.

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