Power-optimal allocation in multi-arm cluster randomized trials
Abstract
This paper discusses a power-based framework for optimal allocation in multi-arm cluster randomized trials with continuous or binary outcomes, allowing cluster sizes and within-cluster correlations to differ across treatment groups. We consider optimal cluster allocation with prespecified cluster sizes as well as joint optimization of cluster allocations and sizes. Using generalized estimating equations to account for correlated outcomes and a quasi-score test to assess treatment homogeneity, we derive locally optimal designs, which depend on specified parameter values. Since locally optimal designs can be highly sensitive to changes in the parameter values, we also propose maximin designs, which maximize the minimum power over a prescribed parameter space and provide robust allocations under parameter uncertainty. We analytically characterize the maximin designs and the least favorable configurations of treatment means and provide procedures for determining the sample size required to attain a target power. Applications to the WASH Benefits Bangladesh and SCORE trials illustrate gains in calculated power and reductions in the required sample size relative to the original trial designs.
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BibTeX
@misc{wani2026poweroptimal,
title = {{Power-optimal allocation in multi-arm cluster randomized trials}},
author = {Muneeb Ahmad Wani and Satya Prakash Singh},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.00842},
eprint = {2611.00842},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.00842},
}
RIS
TY - UNPB
TI - Power-optimal allocation in multi-arm cluster randomized trials
AU - Muneeb Ahmad Wani
AU - Satya Prakash Singh
PY - 2026
DA - 2026/11/01
PB - arXiv
DO - 10.48550/arxiv.2611.00842
UR - https://arxiv.org/abs/2611.00842
AB - This paper discusses a power-based framework for optimal allocation in multi-arm cluster randomized trials with continuous or binary outcomes, allowing cluster sizes and within-cluster correlations to differ across treatment groups. We consider optimal cluster allocation with prespecified cluster sizes as well as joint optimization of cluster allocations and sizes. Using generalized estimating equations to account for correlated outcomes and a quasi-score test to assess treatment homogeneity, we derive locally optimal designs, which depend on specified parameter values. Since locally optimal designs can be highly sensitive to changes in the parameter values, we also propose maximin designs, which maximize the minimum power over a prescribed parameter space and provide robust allocations under parameter uncertainty. We analytically characterize the maximin designs and the least favorable configurations of treatment means and provide procedures for determining the sample size required to attain a target power. Applications to the WASH Benefits Bangladesh and SCORE trials illustrate gains in calculated power and reductions in the required sample size relative to the original trial designs.
ER -