Grayling Michael J, Mander Adrian P, Wason James M S
MRC Biostatistics Unit, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, UK.
Institute of Health and Society, Newcastle University, Baddiley-Clark Building, Newcastle upon Tyne, UK.
Biom J. 2018 Sep;60(5):903-916. doi: 10.1002/bimj.201700125. Epub 2018 Aug 3.
The ability to accurately estimate the sample size required by a stepped-wedge (SW) cluster randomized trial (CRT) routinely depends upon the specification of several nuisance parameters. If these parameters are misspecified, the trial could be overpowered, leading to increased cost, or underpowered, enhancing the likelihood of a false negative. We address this issue here for cross-sectional SW-CRTs, analyzed with a particular linear-mixed model, by proposing methods for blinded and unblinded sample size reestimation (SSRE). First, blinded estimators for the variance parameters of a SW-CRT analyzed using the Hussey and Hughes model are derived. Following this, procedures for blinded and unblinded SSRE after any time period in a SW-CRT are detailed. The performance of these procedures is then examined and contrasted using two example trial design scenarios. We find that if the two key variance parameters were underspecified by 50%, the SSRE procedures were able to increase power over the conventional SW-CRT design by up to 41%, resulting in an empirical power above the desired level. Thus, though there are practical issues to consider, the performance of the procedures means researchers should consider incorporating SSRE in to future SW-CRTs.
准确估计阶梯楔形（SW）整群随机试验（CRT）所需样本量的能力通常取决于几个干扰参数的设定。如果这些参数设定错误，试验可能会效能过高，导致成本增加，或者效能不足，增加假阴性的可能性。在此，我们针对横断面SW - CRTs，通过提出盲法和非盲法样本量重新估计（SSRE）方法，使用特定的线性混合模型进行分析，来解决这个问题。首先，推导使用赫西和休斯模型分析的SW - CRT方差参数的盲法估计量。在此之后，详细说明SW - CRT在任何时间段后的盲法和非盲法SSRE程序。然后，使用两个示例试验设计场景来检验和对比这些程序的性能。我们发现，如果两个关键方差参数设定不足50%，SSRE程序能够使效能比传统SW - CRT设计提高多达41%，从而使经验效能高于期望水平。因此，尽管有实际问题需要考虑，但这些程序的性能意味着研究人员应考虑将SSRE纳入未来的SW - CRTs中。
