A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes.

ABSTRACT:

Background

Precision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs.

Methods

To address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the "borrowing of information" across the multivariate outcomes, and results in a more ac

SUBMITTER: Wu D 

PROVIDER: S-EPMC11437666 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature