Group lasso priors for Bayesian accelerated failure time models with left-truncated and interval-censored data.

ABSTRACT: An important task in health research is to characterize time-to-event outcomes such as disease onset or mortality in terms of a potentially high-dimensional set of risk factors. For example, prospective cohort studies of Alzheimer's disease (AD) typically enroll older adults for observation over several decades to assess the long-term impact of genetic and other factors on cognitive decline and mortality. The accelerated failure time model is particularly well-suited to such studies, structuring covariate effects as "horizontal" changes to the survival quantiles that conceptually reflect shifts in the outcome distribution due to lifelong exposures. However, this modeling task is complicated by the enrollment of adults at differing ages, and intermittent follow-up visits leading to interval

SUBMITTER: Reeder HT 

PROVIDER: S-EPMC11833807 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature