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Assessing exposure‐time treatment effect heterogeneity in stepped‐wedge cluster randomized trials

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  • Lara Maleyeff
  • Fan Li
  • Sebastien Haneuse
  • Rui Wang

Abstract

A stepped‐wedge cluster randomized trial (CRT) is a unidirectional crossover study in which timings of treatment initiation for clusters are randomized. Because the timing of treatment initiation is different for each cluster, an emerging question is whether the treatment effect depends on the exposure time, namely, the time duration since the initiation of treatment. Existing approaches for assessing exposure‐time treatment effect heterogeneity either assume a parametric functional form of exposure time or model the exposure time as a categorical variable, in which case the number of parameters increases with the number of exposure‐time periods, leading to a potential loss in efficiency. In this article, we propose a new model formulation for assessing treatment effect heterogeneity over exposure time. Rather than a categorical term for each level of exposure time, the proposed model includes a random effect to represent varying treatment effects by exposure time. This allows for pooling information across exposure‐time periods and may result in more precise average and exposure‐time‐specific treatment effect estimates. In addition, we develop an accompanying permutation test for the variance component of the heterogeneous treatment effect parameters. We conduct simulation studies to compare the proposed model and permutation test to alternative methods to elucidate their finite‐sample operating characteristics, and to generate practical guidance on model choices for assessing exposure‐time treatment effect heterogeneity in stepped‐wedge CRTs.

Suggested Citation

  • Lara Maleyeff & Fan Li & Sebastien Haneuse & Rui Wang, 2023. "Assessing exposure‐time treatment effect heterogeneity in stepped‐wedge cluster randomized trials," Biometrics, The International Biometric Society, vol. 79(3), pages 2551-2564, September.
  • Handle: RePEc:bla:biomet:v:79:y:2023:i:3:p:2551-2564
    DOI: 10.1111/biom.13803
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    References listed on IDEAS

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    1. Kelsey L. Grantham & Andrew B. Forbes & Stephane Heritier & Jessica Kasza, 2020. "Time Parameterizations in Cluster Randomized Trial Planning," The American Statistician, Taylor & Francis Journals, vol. 74(2), pages 184-189, April.
    2. Baey, Charlotte & Cournède, Paul-Henry & Kuhn, Estelle, 2019. "Asymptotic distribution of likelihood ratio test statistics for variance components in nonlinear mixed effects models," Computational Statistics & Data Analysis, Elsevier, vol. 135(C), pages 107-122.
    3. Noh, Maengseok & Lee, Youngjo, 2007. "REML estimation for binary data in GLMMs," Journal of Multivariate Analysis, Elsevier, vol. 98(5), pages 896-915, May.
    4. Alecia Nickless & Merryn Voysey & John Geddes & Ly-Mee Yu & Thomas R Fanshawe, 2018. "Mixed effects approach to the analysis of the stepped wedge cluster randomised trial—Investigating the confounding effect of time through simulation," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-22, December.
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