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. 2020 Sep 18;49(3):638-655.
doi: 10.1080/02664763.2020.1822303. eCollection 2022.

A Bayesian shared parameter model for joint modeling of longitudinal continuous and binary outcomes

T Baghfalaki  1 , M Ganjali  2 , A Kabir  3 , A Pazouki  3   4   5
Affiliations

A Bayesian shared parameter model for joint modeling of longitudinal continuous and binary outcomes

T Baghfalaki et al. J Appl Stat. .

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.

Keywords: Conditional model; MCMC methods; intermittent missingness; joint modeling; longitudinal data; mixed-effects model.

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Conflict of interest statement

No potential conflict of interest was reported by the author(s).

Figures

Figure 1.
Figure 1.
Profiles of BMI measurements over time for different types of laparoscopic surgery superimposed by mean over all observed individuals at each time shown by black line. (a) Mini-gastric bypass surgery, (b) RY gastric bypass surgery, (c) other laparoscopic surgery.
Figure 2.
Figure 2.
Pattern of missingness for BMI (left panel) and complication due to surgery (right panel). Each row represents a pattern of missingness at each time point. Red color represents observed values and gray color represents missingness for the occasion time.
Figure 3.
Figure 3.
ROC curves of observed and predicted values by two competing approaches of complication due to surgery for the morbid obesity data.
See this image and copyright information in PMC

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