论文标题
SWDPWR:SAS宏和R套件用于阶梯楔形群集随机试验中的电源计算
swdpwr: A SAS Macro and An R Package for Power Calculation in Stepped Wedge Cluster Randomized Trials
论文作者
论文摘要
背景和目标:阶梯楔形群集随机试验是一种越来越多地用于公共卫生干预评估的研究设计。以前的大多数文献都集中在这种特定类型的群集随机试验的功率计算上,以进行连续结果,以及与二元结果方法的近似值。尽管对二元结果不准确,但已被广泛使用。为了改善二进制结果的近似值,最近发布了两种二元结果阶梯楔设计(SWD)的新方法。但是,这些新方法尚未在公开可用的软件中实施。本文的目的是在各种环境中为连续和二进制结果介绍SWD的电源计算软件。 方法:我们已经开发了SAS宏%swdpwr和一个R软件包SWDPWR用于SWD中的功率计算。在此软件中可以容纳不同的方案,包括横截面和队列设计,二进制和连续结果,边际和条件模型,三个链接函数,以及具有时间效果的三个链接函数。 结果:SWDPWR提供了一种有效的工具,可以在阶梯楔形群集随机跟踪的设计和分析中支持研究人员。 SWDPWR解决了新提出的方法与其应用之间的实现差距,以获取SWD中更准确的功率计算。 结论:此用户友好的软件使新方法更容易访问,并结合了当前可用的许多变化,而其他相关软件包中不支持这些变化。 SWDPWR在两个平台下实施:SAS和R,满足来自各种背景的调查人员的需求。
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.