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Bayesian joint modeling of multivariate longitudinal and survival outcomes using Gaussian copulas
Seoyoon Cho1, Matthew A Psioda2, Joseph G Ibrahim1
1Department of Biostatistics, University of North Carolina, McGavran-Greenberg Hall, CB#7420, Chapel Hill, NC 27599, United States.
Biostatistics (Oxford, England)
|April 26, 2024
Summary
This study introduces a novel Gaussian copula joint model for analyzing longitudinal and survival data. The proposed structured decomposition improves efficiency and reduces complexity in statistical analysis.
Area of Science:
- Biostatistics
- Statistical Modeling
- Survival Analysis
Background:
- Joint models analyze longitudinal and survival data.
- Random effects models present implementation challenges.
- Copulas offer a flexible alternative for joint modeling.
Purpose of the Study:
- Develop a joint model using a Gaussian copula for multivariate longitudinal and survival outcomes.
- Propose a novel decomposition for the copula's correlation structure.
- Enhance efficiency and reduce computational complexity.
Main Methods:
- Gaussian copula for joint modeling.
- Structured correlation decomposition (e.g., auto-regressive).
- Markov chain Monte Carlo (MCMC) for parameter estimation.
Main Results:
- The proposed structured decomposition offers efficiency gains.
- Reduced computational complexity compared to unstructured models.
- Successful application in a simulation study and real-world breast cancer trial data.
Conclusions:
- The novel Gaussian copula joint model with structured decomposition is effective.
- This approach provides a valuable tool for analyzing complex longitudinal and survival data.
- Demonstrated utility in biostatistical research and clinical trial analysis.
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