Abstract
BackgroundStepped-wedge designs (SWD) are increasingly used to evaluate the impact of changes to the process of care within health care systems. However, to generate definitive evidence, a correct sample size calculation is crucial to ensure such studies are properly powered. The seminal work of Hussey and Hughes (Contemp Clin Trials 28(2):182–91, 2004) provides an analytical formula for power calculations with normal outcomes using a linear model and simple random effects. However, minimal development and evaluation have been done for power calculation with non-normal outcomes on their natural scale (e.g., logit, log). For example, binary endpoints are common, and logistic regression is the natural multilevel model for such clustered data.MethodsWe propose a power calculation formula for SWD with either normal or non-normal outcomes in the context of generalized linear mixed models by adopting the Laplace approximation detailed in Breslow and Clayton (J Am Stat Assoc 88(421):9–25, 1993) to obtain the covariance matrix of the estimated parameters.ResultsWe compare the performance of our proposed method with simulation-based sample size calculation and demonstrate its use on a study of patient-delivered partner therapy for STI treatment and a study that assesses the impact of providing additional benchmark prevalence information in a radiologic imaging report. To facilitate adoption of our methods we also provide a function embedded in the R package “swCRTdesign” for sample size and power calculation for multilevel stepped-wedge designs.ConclusionsOur method requires minimal computational power. Therefore, the proposed procedure facilitates rapid dynamic updates of sample size calculations and can be used to explore a wide range of design options or assumptions.
Highlights
Stepped-wedge designs (SWD) are increasingly used to evaluate the impact of changes to the process of care within health care systems
To facilitate the use of penalized quasi-likelihood (PQL)-based sample size calculation in SWDs with nonnormal outcomes, we propose a sample size and power calculation formula for SWDs with normal or non-normal outcomes by simplifying the Laplace approximation of the covariance matrix of the estimated parameter of interest
To facilitate adoption of our methods, we provide a function embedded in the R package “swCRTdesign” [21] for sample size and power calculation for multilevel stepped wedge designs
Summary
Stepped-wedge designs (SWD) are increasingly used to evaluate the impact of changes to the process of care within health care systems. The seminal work of Hussey and Hughes (Contemp Clin Trials 28(2):182–91, 2004) provides an analytical formula for power calculations with normal outcomes using a linear model and simple random effects. Minimal development and evaluation have been done for power calculation with non-normal outcomes on their natural scale (e.g., logit, log). Stepped-wedge designs (SWD) are a type of contemporary and novel CRT that have been used to evaluate new interventions and programs deployed in the context. In SWD, all clusters (typically) start in the control group, cross over to the intervention group at different time points, and stay on intervention until the end of the trial. The time at which each cluster starts the intervention is randomized. Either different individuals (cross-sectional design) from each cluster may be measured at different time points or the same individuals may be repeatedly assessed (cohort design)
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