Optimizing a Bayesian hierarchical adaptive platform trial design for stroke patients.

ABSTRACT:

Background

Platform trials are well-known for their ability to investigate multiple arms on heterogeneous patient populations and their flexibility to add/drop treatment arms due to efficacy/lack of efficacy. Because of their complexity, it is important to develop highly optimized, transparent, and rigorous designs that are cost-efficient, offer high statistical power, maximize patient benefit, and are robust to changes over time.

Methods

To address these needs, we present a Bayesian platform trial design based on a beta-binomial model for binary outcomes that uses three key strategies: (1) hierarchical modeling of subgroups within treatment arms that allows for borrowing of information across subgroups, (2) utilization of response-adaptive randomization (RAR) schemes that s

SUBMITTER: Gao G 

PROVIDER: S-EPMC9446515 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature