Model-robust standardization in stepped wedge designs
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912997001134080 |
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| author | Fang, Xi Wang, Xueqi Heagerty, Patrick J. Wang, Bingkai Li, Fan |
| author_facet | Fang, Xi Wang, Xueqi Heagerty, Patrick J. Wang, Bingkai Li, Fan |
| contents | Stepped-wedge cluster-randomized trials (SW-CRTs) are widely used in healthcare and implementation science, providing an ethical advantage by ensuring all clusters eventually receive the intervention. The staggered rollout of treatment introduces complexities in defining and estimating treatment effect estimands, particularly under informative sizes. Traditional model-based methods, including generalized estimating equations (GEE) and linear mixed models (LMM), produce estimates that depend on implicit weighting schemes and parametric assumptions, leading to bias for different types of estimands in the presence of informative sizes. While recent methods have attempted to provide robust estimation in SW-CRTs, they are restrictive on modeling assumptions or lack of general framework for consistent estimating multiple estimands under informative size. In this article, we propose a model-robust standardization framework for SW-CRTs that generalizes existing methods from parallel-arm CRTs. We define causal estimands including horizontal-individual, horizontal-cluster, vertical-individual, and vertical-cluster average treatment effects under a super population framework and introduce an augmented standardization estimator that standardizes parametric and semiparametric working models while maintaining robustness to informative cluster size under arbitrary misspecification. We evaluate the finite-sample properties of our proposed estimators through extensive simulation studies, assessing their performance under various SW-CRT designs. Finally, we illustrate the practical application of model-robust estimation through a reanalysis of two real-world SW-CRTs. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_17190 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model-robust standardization in stepped wedge designs Fang, Xi Wang, Xueqi Heagerty, Patrick J. Wang, Bingkai Li, Fan Methodology Stepped-wedge cluster-randomized trials (SW-CRTs) are widely used in healthcare and implementation science, providing an ethical advantage by ensuring all clusters eventually receive the intervention. The staggered rollout of treatment introduces complexities in defining and estimating treatment effect estimands, particularly under informative sizes. Traditional model-based methods, including generalized estimating equations (GEE) and linear mixed models (LMM), produce estimates that depend on implicit weighting schemes and parametric assumptions, leading to bias for different types of estimands in the presence of informative sizes. While recent methods have attempted to provide robust estimation in SW-CRTs, they are restrictive on modeling assumptions or lack of general framework for consistent estimating multiple estimands under informative size. In this article, we propose a model-robust standardization framework for SW-CRTs that generalizes existing methods from parallel-arm CRTs. We define causal estimands including horizontal-individual, horizontal-cluster, vertical-individual, and vertical-cluster average treatment effects under a super population framework and introduce an augmented standardization estimator that standardizes parametric and semiparametric working models while maintaining robustness to informative cluster size under arbitrary misspecification. We evaluate the finite-sample properties of our proposed estimators through extensive simulation studies, assessing their performance under various SW-CRT designs. Finally, we illustrate the practical application of model-robust estimation through a reanalysis of two real-world SW-CRTs. |
| title | Model-robust standardization in stepped wedge designs |
| topic | Methodology |
| url | https://arxiv.org/abs/2507.17190 |