swdpwr: A SAS Macro and An R Package for Power Calculation in Stepped Wedge Cluster Randomized Trials

TL;DR

This paper introduces swdpwr, a software package in SAS and R that enables accurate power calculations for stepped wedge cluster randomized trials with both continuous and binary outcomes, accommodating various study designs.

Contribution

It provides the first publicly available implementation of recent advanced methods for power calculation in SWDs, supporting diverse study scenarios.

Findings

Supports various designs including cross-sectional and cohort

Handles binary and continuous outcomes with multiple models

Facilitates more accurate power calculations for SWDs

Abstract

Background and objective: The stepped wedge cluster randomized trial is a study design increasingly used for public health intervention evaluations. Most previous literature focuses on power calculations for this particular type of cluster randomized trials for continuous outcomes, along with an approximation to this approach for binary outcomes. Although not accurate for binary outcomes, it has been widely used. To improve the approximation for binary outcomes, two new methods for stepped wedge designs (SWDs) of binary outcomes have recently been published. However, these new methods have not been implemented in publicly available software. The objective of this paper is to present power calculation software for SWDs in various settings for both continuous and binary outcomes. Methods: We have developed a SAS macro %swdpwr and an R package swdpwr for power calculation in SWDs. Different scenarios including cross-sectional and cohort designs, binary and continuous outcomes, marginal and conditional models, three link functions, with and without time effects are accommodated in this software. Results: swdpwr provides an efficient tool to support investigators in the design and analysis of stepped wedge cluster randomized trails. swdpwr addresses the implementation gap between newly proposed methodology and their application to obtain more accurate power calculations in SWDs. Conclusions: This user-friendly software makes the new methods more accessible and incorporates as many variations as currently available, which were not supported in other related packages. swdpwr is implemented under two platforms: SAS and R, satisfying the needs of investigators from various backgrounds.