Joint modelling of longitudinal and survival data in the presence of competing risks with applications to prostate cancer data. (February 2021)

• Record Type: Journal Article
• Authors: Sheikh, Md Tuhin; Ibrahim, Joseph G; Gelfond, Jonathan A; Sun, Wei; Chen, Ming-Hui
• Other Names: Armero Carmen guest-editor.; Gómez-Rubio Virgilio guest-editor.
• Is Part Of: Statistical modelling. Volume 21:Number 1/2(2021)
• Page Start: 72
• Page End: 94
• Publication Date: 2021-02
• Subjects: cause-specific competing risks model -- DIC -- mixed effects model -- reparametrization -- SELECT data -- WAIC; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X20944620
• Languages: English
• ISSNs: 1471-082X

Abstract

This research is motivated from the data from a large Selenium and Vitamin E Cancer Prevention Trial (SELECT). The prostate specific antigens (PSAs) were collected longitudinally, and the survival endpoint was the time to low-grade cancer or the time to high-grade cancer (competing risks). In this article, the goal is to model the longitudinal PSA data and the time-to-prostate cancer (PC) due to low- or high-grade. We consider the low-grade and high-grade as two competing causes of developing PC. A joint model for simultaneously analysing longitudinal and time-to-event data in the presence of multiple causes of failure (or competing risk) is proposed within the Bayesian framework. The proposed model allows for handling the missing causes of failure in the SELECT data and implementing an efficient Markov chain Monte Carlo sampling algorithm to sample from the posterior distribution via a novel reparameterization technique. Bayesian criteria, Δ DIC Surv, andΔ WAIC Surv, are introduced to quantify the gain in fit in the survival sub-model due to the inclusion of longitudinal data. A simulation study is conducted to examine the empirical performance of the posterior estimates as well asΔ DIC Surv andΔ WAIC Surv and a detailed analysis of the SELECT data is also carried out to further demonstrate the proposed methodology.