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Activity Number: 233 - Innovations in Inferential and Design Strategies in Mental Health Research
Type: Contributed
Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
Sponsor: Mental Health Statistics Section
Abstract #323693 View Presentation
Title: Accelerated Failure Time Models for Semi-Competing Risks Data in the Presence of Complex Censoring
Author(s): Sebastien Haneuse* and Kyu Ha Lee
Companies: TE-Harvard T.H. Chan School of Public Health and The Forsyth Institute
Keywords: Alzheimer's Disease ; Semi-competing risks ; Accelerated failure time models ; Left trunction ; Interval censoring ; MCMC
Abstract:

Statistical analyses that investigate risk factors for Alzheimer's disease (AD) often require accommodating complex censoring including left-truncation and/or either interva. Additional challenges arise due to the fact death, as a competing force, may not be independent of AD. Towards resolving the latter, one may embed the analysis within the semi-competing risks framework, specifically using the so-called illness-death model. To the best of our knowledge, however, the semi-competing risks literature has not fully considered analyses in contexts with complex censoring, as in studies of AD. This is particularly the case when interest lies with the accelerated failure time (AFT) model, an alternative to the traditional multiplicative Cox model. We present a new Bayesian framework for estimation/inference of an AFT illness-death model for semi-competing risks data subject to complex censoring. An efficient computational algorithm that gives researchers the flexibility to adopt either a fully parametric or a semi-parametric model specification is developed and implemented. The methods are motivated by and illustrated with the Adult Changes in Thought study.


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

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