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Power analyses for stepped wedge designs with multivariate continuous outcomes.

Kendra Davis-Plourde1,2,3, Monica Taljaard4,5, Fan Li1,3

  • 1Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.

Statistics in Medicine
|December 24, 2022
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Summary
This summary is machine-generated.

Researchers developed new methods for sample size and power calculations in stepped wedge cluster randomized trials with multiple outcomes. These methods improve efficiency and provide a rigorous justification for using multivariate models in such complex trial designs.

Keywords:
cluster randomized trialco-primary endpointsmultivariate linear mixed modelsample size estimationstepped wedge trial

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Public Health Research

Background:

  • Multivariate outcomes are frequently encountered in pragmatic cluster randomized trials.
  • Existing sample size methods for multivariate outcomes are limited to parallel designs, not stepped wedge designs.
  • There is a need for robust statistical procedures for stepped wedge cluster randomized trials (SW-CRTs) involving multiple endpoints.

Purpose of the Study:

  • To present computationally efficient power and sample size procedures for SW-CRTs with multivariate outcomes.
  • To address the distinct within-period and between-period intracluster correlation coefficients (ICCs) in SW-CRTs.
  • To provide a theoretical justification for using multivariate linear mixed models in SW-CRTs.

Main Methods:

  • Derivation of the joint distribution of intervention test statistics under a multivariate linear mixed model.
  • Application of the derived methods to an intersection-union test for co-primary outcomes.
  • Development of simplifications for common treatment effects and ICCs, and an extension to closed-cohort designs.

Main Results:

  • Computationally efficient procedures for power and sample size calculations in SW-CRTs with multivariate outcomes were developed.
  • The multivariate linear mixed model was formally proven to yield a more efficient treatment effect estimator than univariate models under common ICCs.
  • Simulations validated the proposed methods, demonstrating their practical applicability using existing SW-CRT data.

Conclusions:

  • The proposed methods offer a statistically sound and efficient approach for sample size and power calculations in SW-CRTs with multivariate outcomes.
  • The study provides a rigorous justification for employing multivariate linear mixed models, enhancing analytical power and precision.
  • These advancements are crucial for the design and analysis of complex public health and clinical intervention studies using the stepped wedge design.