Evidence Synthesis for Complex Interventions Using Meta-Regression Models

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 (i) summarize the available evidence and (ii) predict the outcomes of future studies or practice in target populations and settings of interest. We illustrate this modeling approach using data from a systematic review of diabetes quality improvement (QI) interventions involving at least one of 12 QI strategy components. We specified a series of meta-regression models to assess the association of specific components with the post-treatment outcome mean and compared the results to conventional meta-analysis approaches. Compared to conventional approaches, modeling the response surface of study results can better reflect the associations between intervention components and study characteristics with the post-treatment 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.