Article ; Online: Joint frailty modeling of time-to-event data to elicit the evolution pathway of events: a generalized linear mixed model approach.
Biostatistics (Oxford, England)
2021 Volume 24, Issue 1, Page(s) 108–123
Abstract: 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 ... ...
| Abstract | 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. |
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| MeSH term(s) | Humans ; Frailty ; Cross-Sectional Studies ; Survival Analysis ; Computer Simulation ; Linear Models |
| Language | English |
| Publishing date | 2021-11-08 |
| Publishing country | England |
| Document type | Journal Article ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 2031500-4 |
| ISSN | 1468-4357 ; 1465-4644 |
| ISSN (online) | 1468-4357 |
| ISSN | 1465-4644 |
| DOI | 10.1093/biostatistics/kxab037 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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