Calibrated Bayesian inference for overall and subgroup-specific causal effects in stepped-wedge cluster-randomized trials
Abstract
Stepped-wedge cluster-randomized trials (SW-CRTs) randomly assign clusters to sequences that determine when each cluster switches from control to intervention. Conventional Bayesian analyses of SW-CRTs report treatment effect coefficients from a working outcome model, but under model misspecification or informative cluster-period sizes, such coefficients can lack a clear causal interpretation. In this work, we develop Bayesian methods targeting weighted average treatment effect estimands in SW-CRTs, including novel subgroup-specific causal estimands defined by baseline characteristics. We compare model-based g-computation with model-robust standardization, implementing both with linear mixed models and mixed-effects Bayesian additive regression trees as working outcome models, and propose a calibrated variance estimator that augments posterior parameter uncertainty with cluster-level resampling to better capture sampling variability. In simulations with outcome model misspecification and informative cluster-period sizes, model-robust standardization with calibrated inference exhibits negligible bias and near-nominal frequentist coverage for overall and subgroup-specific causal effects, whereas g-computation and uncalibrated intervals become insufficient. We illustrate the proposed methods with a reanalysis of the Trauma Survivors Outcomes and Support trial.
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BibTeX
@misc{liu2026calibrated,
title = {{Calibrated Bayesian inference for overall and subgroup-specific causal effects in stepped-wedge cluster-randomized trials}},
author = {Ruyi Liu and Yuki Ohnishi and Xi Fang and Michael J. Daniels and Fan Li},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.20858},
eprint = {2610.20858},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.20858},
}
RIS
TY - UNPB
TI - Calibrated Bayesian inference for overall and subgroup-specific causal effects in stepped-wedge cluster-randomized trials
AU - Ruyi Liu
AU - Yuki Ohnishi
AU - Xi Fang
AU - Michael J. Daniels
AU - Fan Li
PY - 2026
DA - 2026/10/14
PB - arXiv
DO - 10.48550/arxiv.2610.20858
UR - https://arxiv.org/abs/2610.20858
AB - Stepped-wedge cluster-randomized trials (SW-CRTs) randomly assign clusters to sequences that determine when each cluster switches from control to intervention. Conventional Bayesian analyses of SW-CRTs report treatment effect coefficients from a working outcome model, but under model misspecification or informative cluster-period sizes, such coefficients can lack a clear causal interpretation. In this work, we develop Bayesian methods targeting weighted average treatment effect estimands in SW-CRTs, including novel subgroup-specific causal estimands defined by baseline characteristics. We compare model-based g-computation with model-robust standardization, implementing both with linear mixed models and mixed-effects Bayesian additive regression trees as working outcome models, and propose a calibrated variance estimator that augments posterior parameter uncertainty with cluster-level resampling to better capture sampling variability. In simulations with outcome model misspecification and informative cluster-period sizes, model-robust standardization with calibrated inference exhibits negligible bias and near-nominal frequentist coverage for overall and subgroup-specific causal effects, whereas g-computation and uncalibrated intervals become insufficient. We illustrate the proposed methods with a reanalysis of the Trauma Survivors Outcomes and Support trial.
ER -