A Joint Model for (Un)Bounded Longitudinal Markers, Competing Risks, and Recurrent Events Using Patient Registry Data.

ABSTRACT: Joint models for longitudinal and survival data have become a popular framework for studying the association between repeatedly measured biomarkers and clinical events. Nevertheless, addressing complex survival data structures, especially handling both recurrent and competing event times within a single model, remains a challenge. This causes important information to be disregarded. Moreover, existing frameworks rely on a Gaussian distribution for continuous markers, which may be unsuitable for bounded biomarkers, resulting in biased estimates of associations. To address these limitations, we propose a Bayesian shared-parameter joint model that simultaneously accommodates multiple (possibly bounded) longitudinal markers, a recurrent event process, and competing risks. We use the beta distr

SUBMITTER: Miranda Afonso P 

PROVIDER: S-EPMC12023843 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature