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A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes
Danni Wu1,2, Keith S Goldfeld3, Eva Petkova3
1Department of Population Health, New York University Grossman School of Medicine, 180 Madison Avenue, New York, 10016, New York, USA. dw2625@nyu.edu.
BMC Medical Research Methodology
|September 28, 2024
Summary
This study introduces a new Bayesian model for precision medicine that uses multiple health outcomes to improve individualized treatment rules (ITRs). The approach enhances treatment efficacy estimation and reduces incorrect treatment decisions for better personalized care.
Area of Science:
- Biostatistics
- Precision Medicine
- Clinical Trial Analysis
Background:
- Precision medicine utilizes patient characteristics for tailored treatments.
- Current precision medicine often relies on single health outcomes, leading to suboptimal data use for individualized treatment rules (ITRs).
Purpose of the Study:
- To address the limitations of single-outcome approaches in precision medicine.
- To develop a more accurate method for estimating heterogeneous treatment effects and optimizing ITRs.
Main Methods:
- Proposed a Bayesian multivariate hierarchical model to jointly analyze mixed types of correlated health outcomes.
- Facilitated information sharing across multiple outcomes for improved estimation.
- Developed a treatment benefit index based on the multivariate outcome model.
Main Results:
- Simulations showed the proposed method reduces erroneous treatment decisions compared to single-outcome models.
- Sensitivity analyses confirmed model robustness across various scenarios.
- Application to a COVID-19 trial demonstrated improved estimation of individual treatment efficacy and optimal ITRs.
Conclusions:
- The study successfully models mixed health outcomes for developing ITRs.
- Considering multiple outcomes advances the development of more effective personalized treatments.
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