English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In the context of clinical and biomedical studies, joint frailty models havebeen developed to study the joint temporal evolution of recurrent and terminalevents, capturing both the heterogeneous susceptibility to experiencing a newepisode and the dependence between the two processes. Whilediscretely-distributed frailty is usually more exploitable by clinicians andhealthcare providers, existing literature on joint frailty models predominantlyassumes continuous distributions for the random effects. In this article, wepresent a novel joint frailty model that assumes bivariatediscretely-distributed non-parametric frailties, with an unknown finite numberof mass points. This approach facilitates the identification of latentstructures among subjects, grouping them into sub-populations defined by ashared frailty value. We propose an estimation routine viaExpectation-Maximization algorithm, which not only estimates the number ofsubgroups but also serves as an unsupervised classification tool. This work ismotivated by a study of patients with Heart Failure (HF) receiving ACEinhibitors treatment in the Lombardia region of Italy. Recurrent events ofinterest are hospitalizations due to HF and terminal event is death for anycause.

Joint modelling of recurrent and terminal events with  discretely-distributed non-parametric frailty: application on  re-hospitalizations and death in heart failure patients