Modeling semi-competing risks data as a longitudinal bivariate process

TL;DR

This paper introduces a novel regression framework for semi-competing risks data, modeling the dependence between non-terminal and terminal events as a longitudinal bivariate process with clinical interpretability.

Contribution

It proposes a new approach that captures local and global dependence in semi-competing risks, allowing for richer clinical insights and accommodating complex data features.

Findings

Dependence between AD and death can be characterized as local and global.

Gender and APOE-$ extit{ε4}$ influence joint risk of AD and death.

Framework handles right censoring, left truncation, and time-varying covariates.

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, $\textit{local}$ and $\textit{global}$ 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 $\ge$1 APOE-$\epsilon4$ allele on the joint risk of AD and death.