Journal Article

Optimizing a Bayesian hierarchical adaptive platform trial design for stroke patients

Saver, Jeffrey L., Meinzer, Caitlyn, Gajewski, Byron J., Gao, Guangyi, Beall, Jonathan, Wick, Jo

2022

Overview

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 seek a tradeoff between statistical power and patient benefit, and (3) adjustment for potential drift over time. Motivated by a proposed clinical trial that aims to find the appropriate treatment for different subgroup populations of ischemic stroke patients, extensive simulation studies were performed to validate the approach, compare different allocation rules, and study the model operating characteristics. Results and conclusions Our proposed approach achieved high statistical power and good patient benefit and was also robust against population drift over time. Our design provided a good balance between the strengths of both the traditional RAR scheme and fixed 1:1 allocation and may be a promising choice for dichotomous outcomes trials investigating multiple subgroups.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Adaptive Clinical Trials as Topic / Bayes Theorem / Bayesian models / Bayesian statistical decision theory / Beta-binomial / Biomedicine / Borrowing / Care and treatment / Clinical trials / Clinical Trials as Topic / Computer Simulation / Evaluation / Health Sciences / Hierarchical models / Humans / Medicine / Medicine & Public Health / Methodology / Methods / Models, Statistical / Patients / Platform trial design / Random Allocation / Research Design / Response rates / Response-adaptive randomization / Simulated patients / Simulation / Statistical power / Statistics for Life Sciences / Stroke / Stroke - diagnosis / Stroke - therapy / Stroke patients