A Semiparametric Bayesian Joint Modelling of Skewed Longitudinal and Competing Risks Failure Time Data: With Application to Chronic Kidney Disease

Melkamu Molla Ferede (), Samuel Mwalili, Getachew Dagne, Simon Karanja, Workagegnehu Hailu, Mahmoud El-Morshedy and Afrah Al-Bossly
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Melkamu Molla Ferede: Pan African University Institute for Basic Sciences, Technology and Innovation (PAUSTI), Nairobi 62000-00200, Kenya
Samuel Mwalili: Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi 62000-00200, Kenya
Getachew Dagne: Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, 13201 Bruce B. Downs, Tampa, FL 33612, USA
Simon Karanja: School of Public Health, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi 62000-00200, Kenya
Workagegnehu Hailu: Department of Internal Medicine, College of Medicine and Health Sciences, University of Gondar, Gondar 196, Ethiopia
Mahmoud El-Morshedy: Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
Afrah Al-Bossly: Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia

Mathematics, 2022, vol. 10, issue 24, 1-21

Abstract: In clinical and epidemiological studies, when the time-to-event(s) and the longitudinal outcomes are associated, modelling them separately may give biased estimates. A joint modelling approach is required to obtain unbiased results and to evaluate their association. In the joint model, a subject may be exposed to more than one type of failure event (competing risks). Considering the competing event as an independent censoring of the time-to-event process may underestimate the true survival probability and give biased results. Within the joint model, longitudinal outcomes may have nonlinear (irregular) trajectories over time and exhibit skewness with heavy tails. Accordingly, fully parametric mixed-effect models may not be flexible enough to model this type of complex longitudinal data. In addition, assuming a Gaussian distribution for model errors may be too restrictive to adequately represent within-individual variations and may lack robustness against deviation from distributional assumptions. To simultaneously overcome these issues, in this paper, we presented semiparametric joint models for competing risks failure time and skewed-longitudinal data by using a smoothing spline approach and a multivariate skew-t distribution. We also considered different parameterization approaches in the formulation of joint models and used a Bayesian approach to make the statistical inference. We illustrated the proposed methods by analyzing real data on a chronic kidney disease. To evaluate the performance of the methods, we also carried out simulation studies. The results of both the application and simulation studies revealed that the joint modelling approach proposed in this study performed well when the semiparametric, random-effects parameterization, and skew-t distribution specifications were taken into account.

Keywords: skewed-longitudinal data; semiparametric mixed-effects model; competing risks failure time data; joint modelling; chronic kidney disease; Bayesian inference (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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