Sample size determinations for stepped-wedge clinical trials from a three-level data hierarchy perspective. (February 2018)

• Record Type: Journal Article
• Authors: Heo, Moonseong; Kim, Namhee; Rinke, Michael L; Wylie-Rosett, Judith
• Is Part Of: Statistical methods in medical research. Volume 27:Number 2(2018)
• Page Start: 480
• Page End: 489
• Publication Date: 2018-02
• Subjects: Stepped-wedge design -- three level data -- statistical power -- sample size -- design effect -- effect size; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280216632564
• Languages: English
• ISSNs: 0962-2802

Abstract

Stepped-wedge (SW) designs have been steadily implemented in a variety of trials. A SW design typically assumes a three-level hierarchical data structure where participants are nested within times or periods which are in turn nested within clusters. Therefore, statistical models for analysis of SW trial data need to consider two correlations, the first and second level correlations. Existing power functions and sample size determination formulas had been derived based on statistical models for two-level data structures. Consequently, the second-level correlation has not been incorporated in conventional power analyses. In this paper, we derived a closed-form explicit power function based on a statistical model for three-level continuous outcome data. The power function is based on a pooled overall estimate of stratified cluster-specific estimates of an intervention effect. The sampling distribution of the pooled estimate is derived by applying a fixed-effect meta-analytic approach. Simulation studies verified that the derived power function is unbiased and can be applicable to varying number of participants per period per cluster. In addition, when data structures are assumed to have two levels, we compare three types of power functions by conducting additional simulation studies under a two-level statistical model. In this case, the power function based on a sampling distribution of a marginal, as opposed to pooled, estimate of the intervention effect performed the best. Extensions of power functions to binary outcomes are also suggested.