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

T Baghfalaki1, M Ganjali2, A Kabir3

  • 1Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel joint model for mixed biomarkers in longitudinal data, improving clinical decisions. The model effectively handles different missing data patterns and unequal observation times for continuous and binary responses.

Keywords:
Conditional modelMCMC methodsintermittent missingnessjoint modelinglongitudinal datamixed-effects model

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Biomarker Research

Background:

  • Joint modeling of biomarkers enhances clinical decision-making by improving parameter estimation efficiency.
  • Longitudinal studies often involve biomarkers with non-equivalent observation times and disparate missing data patterns.
  • Accurate analysis of such complex data is crucial for reliable clinical insights.

Purpose of the Study:

  • To propose a novel joint model for associated continuous and binary longitudinal responses.
  • To address challenges posed by different missing data patterns and unequal observation times.
  • To enhance the accuracy and efficiency of biomarker analysis in clinical studies.

Main Methods:

  • A conditional model for joint modeling of continuous and binary responses was developed.
  • Two shared random effects models were employed to handle intermittent missingness.
  • Parameter estimation and model implementation were performed using a Bayesian approach with Markov Chain Monte Carlo (MCMC).

Main Results:

  • Simulation studies validated the performance and robustness of the proposed joint model.
  • The model demonstrated effectiveness in handling longitudinal data with mixed response types and complex missingness.
  • The proposed method provided reliable parameter estimates for associated biomarkers.

Conclusions:

  • The developed joint model offers a robust framework for analyzing mixed biomarkers in longitudinal studies with complex data structures.
  • This approach improves the efficiency of parameter estimates, leading to better clinical decision-making.
  • The model's application to bariatric surgery data highlights its practical utility in real-world clinical research.