Hemming Karla, Taljaard Monica, Forbes Andrew
Institute of Applied Health Research, University of Birmingham, Birmingham, B15 2TT, UK.
Clinical Epidemiology Program, Ottawa Hospital Research Institute, 1053 Carling Avenue, Ottawa, ON, K1Y4E9, Canada.
Trials. 2017 Mar 4;18(1):101. doi: 10.1186/s13063-017-1833-7.
The stepped wedge cluster randomised trial (SW-CRT) is increasingly being used to evaluate policy or service delivery interventions. However, there is a dearth of trials literature addressing analytical approaches to the SW-CRT. Perhaps as a result, a significant number of published trials have major methodological shortcomings, including failure to adjust for secular trends at the analysis stage. Furthermore, the commonly used analytical framework proposed by Hussey and Hughes makes several assumptions.
We highlight the assumptions implicit in the basic SW-CRT analytical model proposed by Hussey and Hughes. We consider how simple modifications of the basic model, using both random and fixed effects, can be used to accommodate deviations from the underlying assumptions. We consider the implications of these modifications for the intracluster correlation coefficients. In a case study, the importance of adjusting for the secular trend is illustrated.
The basic SW-CRT model includes a fixed effect for time, implying a common underlying secular trend across steps and clusters. It also includes a single term for treatment, implying a constant shift in this trend under the treatment. When these assumptions are not realistic, simple modifications can be implemented to allow the secular trend to vary across clusters and the treatment effect to vary across clusters or time. In our case study, the naïve treatment effect estimate (adjusted for clustering but unadjusted for time) suggests a beneficial effect. However, after adjusting for the underlying secular trend, we demonstrate a reversal of the treatment effect.
Due to the inherent confounding of the treatment effect with time, analysis of a SW-CRT should always account for secular trends or risk-biased estimates of the treatment effect. Furthermore, the basic model proposed by Hussey and Hughes makes a number of important assumptions. Consideration needs to be given to the appropriate model choice at the analysis stage. We provide a Stata code to implement the proposed analyses in the illustrative case study.
阶梯楔形整群随机试验（SW-CRT）越来越多地用于评估政策或服务提供干预措施。然而，针对SW-CRT分析方法的试验文献匮乏。或许正因如此，大量已发表的试验存在重大方法学缺陷，包括在分析阶段未对长期趋势进行调整。此外，Hussey和Hughes提出的常用分析框架做出了若干假设。
我们强调了Hussey和Hughes提出的基本SW-CRT分析模型中隐含的假设。我们考虑如何通过使用随机效应和固定效应来对基本模型进行简单修改，以适应与基本假设的偏差。我们考虑这些修改对组内相关系数的影响。在一个案例研究中，说明了调整长期趋势的重要性。
基本的SW-CRT模型包括一个时间固定效应，这意味着各阶段和各群组存在共同的潜在长期趋势。它还包括一个单一的治疗项，这意味着在治疗下该趋势会有恒定的变化。当这些假设不现实时，可以进行简单修改，以使长期趋势在各群组间有所不同，且治疗效果在各群组间或不同时间有所变化。在我们的案例研究中，单纯的治疗效果估计值（针对聚类进行了调整，但未针对时间进行调整）表明存在有益效果。然而，在对潜在的长期趋势进行调整后，我们证明治疗效果出现了逆转。
由于治疗效果与时间存在内在的混杂因素，对SW-CRT的分析应始终考虑长期趋势，否则治疗效果估计可能会有偏差。此外，Hussey和Hughes提出的基本模型做出了一些重要假设。在分析阶段需要考虑选择合适的模型。我们提供了一个Stata代码，用于在说明性案例研究中实施所提出的分析。
