Konnyu Kristin J, Grimshaw Jeremy M, Trikalinos Thomas A, Ivers Noah M, Moher David, Dahabreh Issa J
Am J Epidemiol. 2024 Feb 5;193(2):323-338. doi: 10.1093/aje/kwad184.
A goal of evidence synthesis for trials of complex interventions is to inform the design or implementation of novel versions of complex interventions by predicting expected outcomes with each intervention version. Conventional aggregate data meta-analyses of studies comparing complex interventions have limited ability to provide such information. We argue that evidence synthesis for trials of complex interventions should forgo aspirations of estimating causal effects and instead model the response surface of study results to 1) summarize the available evidence and 2) predict the average outcomes of future studies or in new settings. We illustrate this modeling approach using data from a systematic review of diabetes quality improvement (QI) interventions involving at least 1 of 12 QI strategy components. We specify a series of meta-regression models to assess the association of specific components with the posttreatment outcome mean and compare the results to conventional meta-analysis approaches. Compared with conventional approaches, modeling the response surface of study results can better reflect the associations between intervention components and study characteristics with the posttreatment outcome mean. Modeling study results using a response surface approach offers a useful and feasible goal for evidence synthesis of complex interventions that rely on aggregate data.
复杂干预试验的证据合成目标之一是，通过预测每种干预版本的预期结果，为新型复杂干预的设计或实施提供信息。比较复杂干预的研究进行传统的汇总数据荟萃分析，提供此类信息的能力有限。我们认为，复杂干预试验的证据合成应放弃估计因果效应的期望，转而对研究结果的响应面进行建模，以便：1）总结现有证据；2）预测未来研究或新环境中的平均结果。我们使用对糖尿病质量改进（QI）干预措施进行系统评价的数据来说明这种建模方法，这些干预措施涉及12种QI策略组件中的至少一种。我们指定了一系列元回归模型，以评估特定组件与治疗后平均结果之间的关联，并将结果与传统荟萃分析方法进行比较。与传统方法相比，对研究结果的响应面进行建模可以更好地反映干预组件与研究特征和治疗后平均结果之间的关联。使用响应面方法对研究结果进行建模，为依赖汇总数据的复杂干预措施的证据合成提供了一个有用且可行的目标。
