Modeling semi-competing risks data as a longitudinal bivariate process

Abstract

The Adult Changes in Thought (ACT) study is a long-running prospective study of incident all-cause dementia and Alzheimer's disease (AD). As the cohort ages, death (a terminal event) is a prominent competing risk for AD (a non-terminal event), although the reverse is not the case. As such, analyses of data from ACT can be placed within the semi-competing risks framework. Central to semi-competing risks, and in contrast to standard competing risks, is that one can learn about the dependence structure between the two events. To-date, however, most methods for semi-competing risks treat dependence as a nuisance and not a potential source of new clinical knowledge. We propose a novel regression-based framework that views the two time-to-event outcomes through the lens of a longitudinal bivariate process on a partition of the time scale. A key innovation of the framework is that dependence is represented in two distinct forms, local and global dependence, both of which have intuitive clinical interpretations. Estimation and inference are performed via penalized maximum likelihood, and can accommodate right censoring, left truncation and time-varying covariates. The framework is used to investigate the role of gender and having ≥1 APOE-4 allele on the joint risk of AD and death.

Causal inference for semi-competing risks data

“…Starting from 1994, Alzheimer's-free participants of age 65 and older from the Seattle metropolitan area have been recruited. More details can be found elsewhere (Kukull et al, 2002;Nevo et al, 2020). Here, A = 1 means having at least one APOE 4 allele.…”