SWSamp - Simulation-based sample size calculations for a Stepped Wedge Trial (and more). SWSamp is an R package designed to allow a wide range of simulation-based sample size calculations, specifically (but not exclusively!) for a Stepped Wedge Trial (SWT) and is based on the general framework described in Baio et al (2015). In its current version, SWSamp consists of 5 main functions: the first one (which is currently in fact specified by three different commands) performs the analytic sample size calculations using the method of Hussey and Hughes (2007). This can be used as a quick alternative for more standard designs or as a first-order approximation in the case of complicated designs (eg including multiple layers of correlation). The second function replicates the sample size calculations based on the (correct form of) the ”design effect” specified by Woertman et al (2013). This too can be used very effectively in relatively standard cases, but is less efficient in cases of more complex designs. The core functions of SWSamp are make.swt, which can be used to simulate data as obtained by a reasonably wide range of possible SWTs and sim.power, which actually performs the simulation-based computation of the required sample size. More importantly, sim.power can be used to compute the sample size for other data generating processes (DGPs) or designs (ie not specifically for a SWT), which increases the applicability of SWSamp.
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References in zbMATH (referenced in 1 article )
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- Jiachen Chen, Xin Zhou, Fan Li, Donna Spiegelman: swdpwr: A SAS Macro and An R Package for Power Calculation in Stepped Wedge Cluster Randomized Trials (2020) arXiv