Designing and analyzing clinical trials for personalized medicine via Bayesian models.

ABSTRACT: Patients with different characteristics (e.g., biomarkers, risk factors) may have different responses to the same medicine. Personalized medicine clinical studies that are designed to identify patient subgroup treatment efficacies can benefit patients and save medical resources. However, subgroup treatment effect identification complicates the study design in consideration of desired operating characteristics. We investigate three Bayesian adaptive models for subgroup treatment effect identification: pairwise independent, hierarchical, and cluster hierarchical achieved via Dirichlet Process (DP). The impact of interim analysis and longitudinal data modeling on the personalized medicine study design is also explored. Interim analysis is considered since they can accelerate personalized medi

SUBMITTER: Zhang C 

PROVIDER: S-EPMC8084911 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature