Department of Public Health Sciences, Clemson University, Clemson, USA.
University of South Carolina School of Medicine, Greenville, SC, USA.
BMC Med Res Methodol. 2021 Mar 16;21(1):53. doi: 10.1186/s12874-021-01229-6.
Beginning in 2019, stepped-wedge designs (SWDs) were being used in the investigation of interventions to reduce opioid-related deaths in communities across the United States. However, these interventions are competing with external factors such as newly initiated public policies limiting opioid prescriptions, media awareness campaigns, and the COVID-19 pandemic. Furthermore, control communities may prematurely adopt components of the intervention as they become available. The presence of time-varying external factors that impact study outcomes is a well-known limitation of SWDs; common approaches to adjusting for them make use of a mixed effects modeling framework. However, these models have several shortcomings when external factors differentially impact intervention and control clusters.
We discuss limitations of commonly used mixed effects models in the context of proposed SWDs to investigate interventions intended to reduce opioid-related mortality, and propose extensions of these models to address these limitations. We conduct an extensive simulation study of anticipated data from SWD trials targeting the current opioid epidemic in order to examine the performance of these models in the presence of external factors. We consider confounding by time, premature adoption of intervention components, and time-varying effect modification- in which external factors differentially impact intervention and control clusters.
In the presence of confounding by time, commonly used mixed effects models yield unbiased intervention effect estimates, but can have inflated Type 1 error and result in under coverage of confidence intervals. These models yield biased intervention effect estimates when premature intervention adoption or effect modification are present. In such scenarios, models incorporating fixed intervention-by-time interactions with an unstructured covariance for intervention-by-cluster-by-time random effects result in unbiased intervention effect estimates, reach nominal confidence interval coverage, and preserve Type 1 error.
Mixed effects models can adjust for different combinations of external factors through correct specification of fixed and random time effects. Since model choice has considerable impact on validity of results and study power, careful consideration must be given to how these external factors impact study endpoints and what estimands are most appropriate in the presence of such factors.
自 2019 年以来，阶跃楔形设计（SWD）开始用于调查旨在减少美国各地社区阿片类药物相关死亡的干预措施。然而，这些干预措施与新启动的限制阿片类药物处方的公共政策、媒体宣传活动和 COVID-19 大流行等外部因素竞争。此外，随着干预措施的可用组件不断增加，对照社区可能会过早采用这些组件。影响研究结果的随时间变化的外部因素是 SWD 的一个众所周知的局限性；常见的调整方法是利用混合效应建模框架。然而，当外部因素对干预组和对照组有不同影响时，这些模型存在几个缺点。
我们讨论了在针对当前阿片类药物流行的拟议 SWD 中调查旨在降低阿片类药物相关死亡率的干预措施时，常用混合效应模型的局限性，并提出了扩展这些模型以解决这些局限性的方法。我们对针对当前阿片类药物流行的 SWD 试验的预期数据进行了广泛的模拟研究，以检验这些模型在存在外部因素时的性能。我们考虑了时间混杂、干预组件过早采用和时变效应修饰——其中外部因素对干预组和对照组有不同影响。
在存在时间混杂的情况下，常用的混合效应模型会产生无偏的干预效果估计，但可能会导致Ⅰ类错误增加，并导致置信区间覆盖不足。当存在过早的干预采用或效应修饰时，这些模型会产生有偏的干预效果估计。在这种情况下，包含固定干预-时间交互作用和非结构化协方差的模型对于干预-聚类-时间随机效应会产生无偏的干预效果估计，达到名义置信区间覆盖，并保留Ⅰ类错误。
混合效应模型可以通过正确指定固定和随机时间效应来调整不同组合的外部因素。由于模型选择对结果的有效性和研究能力有很大影响，因此必须仔细考虑这些外部因素如何影响研究终点，以及在存在这些因素时哪些估计量最合适。
