Information content of stepped wedge designs under the working independence assumption

Tian, Zibo; Li, Fan

doi:10.1016/j.jspi.2023.106097

Journal of Statistical Planning and Inference

2024

DOI: 10.1016/j.jspi.2023.106097

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Information content of stepped wedge designs under the working independence assumption

Zibo Tian,

Fan Li

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2024

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Cited by 3 publications

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How to achieve model-robust inference in stepped wedge trials with model-based methods?

Wang,

2024

Biometrics

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A stepped wedge design is an unidirectional crossover design where clusters are randomized to distinct treatment sequences. While model-based analysis of stepped wedge designs is a standard practice to evaluate treatment effects accounting for clustering and adjusting for covariates, their properties under misspecification have not been systematically explored. In this article, we focus on model-based methods, including linear mixed models and generalized estimating equations with an independence, simple exchangeable, or nested exchangeable working correlation structure. We study when a potentially misspecified working model can offer consistent estimation of the marginal treatment effect estimands, which are defined nonparametrically with potential outcomes and may be functions of calendar time and/or exposure time. We prove a central result that consistency for nonparametric estimands usually requires a correctly specified treatment effect structure, but generally not the remaining aspects of the working model (functional form of covariates, random effects, and error distribution), and valid inference is obtained via the sandwich variance estimator. Furthermore, an additional g-computation step is required to achieve model-robust inference under non-identity link functions or for ratio estimands. The theoretical results are illustrated via several simulation experiments and re-analysis of a completed stepped wedge cluster randomized trial.

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How to achieve model-robust inference in stepped wedge trials with model-based methods?

Wang,

2024

Biometrics

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Model‐assisted analysis of covariance estimators for stepped wedge cluster randomized experiments

Chen,

2024

Scandinavian J Statistics

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Stepped wedge cluster randomized experiments (SW‐CREs) represent a class of unidirectional crossover designs. Although SW‐CREs have become popular, definitions of estimands and robust methods to target estimands under the potential outcomes framework remain insufficient. To address this gap, we describe a class of estimands that explicitly acknowledge the multilevel data structure in SW‐CREs and highlight three typical members of the estimand class that are interpretable. We then introduce four analysis of covariance (ANCOVA) working models to achieve estimand‐aligned analyses with covariate adjustment. Each ANCOVA estimator is model‐assisted, as its point estimator is consistent even when the working model is misspecified. Under the stepped wedge randomization scheme, we establish the finite population Central Limit Theorem for each estimator. We study the finite‐sample operating characteristics of the ANCOVA estimators in simulations and illustrate their application by analyzing the Washington State Expedited Partner Therapy study.

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Optimal designs using generalized estimating equations in cluster randomized crossover and stepped wedge trials

Liu,

2024

Stat Methods Med Res

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Cluster randomized crossover and stepped wedge cluster randomized trials are two types of longitudinal cluster randomized trials that leverage both the within- and between-cluster comparisons to estimate the treatment effect and are increasingly used in healthcare delivery and implementation science research. While the variance expressions of estimated treatment effect have been previously developed from the method of generalized estimating equations for analyzing cluster randomized crossover trials and stepped wedge cluster randomized trials, little guidance has been provided for optimal designs to ensure maximum efficiency. Here, an optimal design refers to the combination of optimal cluster-period size and optimal number of clusters that provide the smallest variance of the treatment effect estimator or maximum efficiency under a fixed total budget. In this work, we develop optimal designs for multiple-period cluster randomized crossover trials and stepped wedge cluster randomized trials with continuous outcomes, including both closed-cohort and repeated cross-sectional sampling schemes. Local optimal design algorithms are proposed when the correlation parameters in the working correlation structure are known. MaxiMin optimal design algorithms are proposed when the exact values are unavailable, but investigators may specify a range of correlation values. The closed-form formulae of local optimal design and MaxiMin optimal design are derived for multiple-period cluster randomized crossover trials, where the cluster-period size and number of clusters are decimal. The decimal estimates from closed-form formulae can then be used to investigate the performances of integer estimates from local optimal design and MaxiMin optimal design algorithms. One unique contribution from this work, compared to the previous optimal design research, is that we adopt constrained optimization techniques to obtain integer estimates under the MaxiMin optimal design. To assist practical implementation, we also develop four SAS macros to find local optimal designs and MaxiMin optimal designs.

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Information content of stepped wedge designs under the working independence assumption

Cited by 3 publications

References 34 publications

How to achieve model-robust inference in stepped wedge trials with model-based methods?

How to achieve model-robust inference in stepped wedge trials with model-based methods?

Model‐assisted analysis of covariance estimators for stepped wedge cluster randomized experiments

Optimal designs using generalized estimating equations in cluster randomized crossover and stepped wedge trials

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