Joint frailty modeling of time-to-event data to elicit the evolution pathway of events: a generalized linear mixed model approach. (9th November 2021)

• Record Type: Journal Article
• Authors: Ng, Shu Kay; Tawiah, Richard; Mclachlan, Geoffrey J; Gopalan, Vinod
• Is Part Of: Biostatistics. Volume 24:Number 1(2023)
• Page Start: 108
• Page End: 123
• Publication Date: 2021-11-09
• Subjects: Cancer registry data -- Generalized linear mixed models -- Informative censoring -- Mean residual life -- Multimorbidity -- Secondary primary cancer; Medical statistics -- Periodicals; Biometry -- Periodicals; Health risk assessment -- Periodicals; Medicine -- Research -- Statistical methods -- Periodicals; 610.727
• DOI: 10.1093/biostatistics/kxab037
• Languages: English
• ISSNs: 1465-4644

Abstract

Summary: Multimorbidity constitutes a serious challenge on the healthcare systems in the world, due to its association with poorer health-related outcomes, more complex clinical management, increases in health service utilization and costs, but a decrease in productivity. However, to date, most evidence on multimorbidity is derived from cross-sectional studies that have limited capacity to understand the pathway of multimorbid conditions. In this article, we present an innovative perspective on analyzing longitudinal data within a statistical framework of survival analysis of time-to-event recurrent data. The proposed methodology is based on a joint frailty modeling approach with multivariate random effects to account for the heterogeneous risk of failure and the presence of informative censoring due to a terminal event. We develop a generalized linear mixed model method for the efficient estimation of parameters. We demonstrate the capacity of our approach using a real cancer registry data set on the multimorbidity of melanoma patients and document the relative performance of the proposed joint frailty model to the natural competitor of a standard frailty model via extensive simulation studies. Our new approach is timely to advance evidence-based knowledge to address increasingly complex needs related to multimorbidity and develop interventions that are most effective and viable to better help a large number of individuals with multiple conditions.