Abstract:
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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.
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