A Bayesian shared parameter model for joint modeling of longitudinal continuous and binary outcomes. Issue 3 (17th February 2022)

• Record Type: Journal Article
• Authors: Baghfalaki, T.; Ganjali, M.; Kabir, A.; Pazouki, A.
• Is Part Of: Journal of applied statistics. Volume 49:Issue 3(2022)
• Page Start: 638
• Page End: 655
• Publication Date: 2022-02-17
• Subjects: Conditional model -- intermittent missingness -- joint modeling -- longitudinal data -- MCMC methods -- mixed-effects model; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2020.1822303
• Languages: English
• ISSNs: 0266-4763

Abstract

Abstract : Joint modeling of associated mixed biomarkers in longitudinal studies leads to a better clinical decision by improving the efficiency of parameter estimates. In many clinical studies, the observed time for two biomarkers may not be equivalent and one of the longitudinal responses may have recorded in a longer time than the other one. In addition, the response variables may have different missing patterns. In this paper, we propose a new joint model of associated continuous and binary responses by accounting different missing patterns for two longitudinal outcomes. A conditional model for joint modeling of the two responses is used and two shared random effects models are considered for intermittent missingness of two responses. A Bayesian approach using Markov Chain Monte Carlo (MCMC) is adopted for parameter estimation and model implementation. The validation and performance of the proposed model are investigated using some simulation studies. The proposed model is also applied for analyzing a real data set of bariatric surgery.