A Simulation Study of Statistical Approaches to Data Analysis in the Stepped Wedge Design
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A Simulation Study of Statistical Approaches to Data Analysis in the Stepped Wedge Design Yuqi Ren1 · James P. Hughes2 · Patrick J. Heagerty3 Received: 12 October 2018 / Revised: 8 August 2019 / Accepted: 9 October 2019 © International Chinese Statistical Association 2019
Abstract This paper studies model-based and design-based approaches for the analysis of data arising from a stepped wedge randomized design. Specifically, for different scenarios we compare robustness, efficiency, Type I error rate under the null hypothesis, and power under the alternative hypothesis for the leading analytical options including generalized estimating equations (GEE) and linear mixed model (LMM)-based approaches. We find that GEE models with exchangeable correlation structures are more efficient than GEE models with independent correlation structures under all scenarios considered. The model-based GEE Type I error rate can be inflated when applied with a small number of clusters, but this problem can be solved using a design-based approach. As expected, correct model specification is more important for LMM (compared to GEE) since the model is assumed correct when standard errors are calculated. However, in contrast to the model-based results, the designbased Type I error rates for LMM models under scenarios with a random treatment effect show Type I error inflation even though the fitted models perfectly match the corresponding data-generating scenarios. Therefore, greater robustness can be realized by combining GEE and permutation testing strategies. Keywords Stepped wedge design · GEE · LMM · Permutation test · Simulation
* James P. Hughes [email protected] 1
University of Washington, 4550 11th Ave NE Apt W209, Seattle, WA 98105, USA
2
University of Washington, H655F, Health Sciences Building, 1705 NE Pacific Street, Seattle, WA 98195, USA
3
University of Washington, Box 357232, 1959 NE Pacific Street, Seattle, WA 98195, USA
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Statistics in Biosciences
1 Introduction A stepped wedge cluster randomized trial design is a type of one-way crossover design in which each cluster starts under a reference or control condition and then crosses over to a treatment condition at a randomly determined time point [6]. Eventually, at the last time point, all clusters receive treatment during the final study time period. The unique control to treatment crossover patterns are referred to as “sequences” (e.g., in Fig. 1, the stepped wedge design has 4 sequences). In contrast, in a parallel cluster randomized design, half of the clusters are (usually) randomly assigned to the intervention and half to the control at the beginning of the trial with no planned crossover. A stepped wedge design is also different from a cluster randomized crossover design in which each cluster is randomly assigned to cross over from control to treatment or treatment to control (possibly more than once). In both crossover and stepped wedge trials, a washout time period may be included between intervention and control periods i
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