• Bayesian Response-Adaptive Ran...

Treffer: Bayesian Response-Adaptive Randomization for Cluster Randomized Controlled Trials.

Title:
Bayesian Response-Adaptive Randomization for Cluster Randomized Controlled Trials.
Authors:
Liu Y; Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, California, USA., Young Karris M; Department of Medicine, University of California, San Diego, California, USA., Jain S; Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, California, USA.
Source:
Statistics in medicine [Stat Med] 2026 Jan; Vol. 45 (1-2), pp. e70386.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE
Imprint Name(s):
Original Publication: Chichester ; New York : Wiley, c1982-
MeSH Terms:
Randomized Controlled Trials as Topic*/methods , Randomized Controlled Trials as Topic*/statistics & numerical data, Humans ; Bayes Theorem ; Computer Simulation ; Monte Carlo Method ; Markov Chains ; Cluster Analysis ; HIV Infections/drug therapy ; Research Design ; Models, Statistical
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Grant Information:
P30 AI036214 United States AI NIAID NIH HHS; P30 AI036214 United States NH NIH HHS
Contributed Indexing:
Keywords: Bayesian response‐adaptive randomization; Markov chain Monte Carlo; Thompson sampling; cluster randomized trial
Entry Date(s):
Date Created: 20260122 Date Completed: 20260122 Latest Revision: 20260125
Update Code:
20260125
PubMed Central ID:
PMC12824830
DOI:
10.1002/sim.70386
PMID:
41567115
Database:
MEDLINE

Weitere Informationen

Cluster randomized controlled trials where groups (or clusters) of individuals, rather than single individuals, are randomized are especially useful when individual-level randomization is not feasible or when interventions are naturally delivered at the group level. Balanced randomization in the cluster randomized trial setting can pose logistical challenges and strain resources if subjects are randomized to a non-optimal arm. We propose a Bayesian response-adaptive randomization design for cluster randomized controlled trials based on Thompson sampling, which dynamically allocates clusters to the most efficacious treatment arm based on the interim posterior distributions of treatment effects using Markov chain Monte Carlo sampling. Our design also incorporates early stopping rules for efficacy and futility determined by prespecified posterior probability thresholds. The performance of the proposed design is evaluated across various operating characteristics under multiple settings, including varying intra-cluster correlation coefficients, cluster sizes, and effect sizes. Our adaptive approach is also compared with a standard, parallel two-arm cluster randomized controlled clinical trial design, highlighting improvements in both ethical considerations and efficiency. From our simulation studies based on an HIV behavioral trial, we demonstrate these improvements by preferentially assigning more clusters to the more efficacious intervention while maintaining robust statistical power and controlling false positive rates.
(© 2026 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)