Methods for generalizing inferences from cluster randomized controlled trials to target populations

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PROJECT SUMMARY/ABSTRACT Cluster trials are the study design of choice when interventions are best applied at the group level and when exposure of one individual may affect the outcomes of other individuals in the same cluster. Cluster trials are increasingly embedded within large health care systems, allowing the use of routinely collected data to increase research efficiency. There is concern, however – and this proposal provides supportive evidence – that randomized clusters are not representative the target populations seen in routine care. When treatment effects vary over factors that influence trial participation, treatment effects from the trial cannot be directly applied to real-world target populations of substantive interest. Thus, even in well-designed cluster trials, selective participation can lead to bias in drawing causal inferences about the target population. Given the increasing number of cluster trials being conducted, investigators need rigorous methods for generalizing findings from cluster trials to target populations that address selective participation bias and can account for multiple data science challenges, including stochastic dependence among observations in the same cluster; availability of randomized trial data from only a few clusters or from clusters with relatively small sample sizes; lack of knowledge of predictors of trial participation and the outcome, when candidate covariates often exceed the number of available clusters and necessitate the use of flexible machine learning approaches; and missing outcome data. In response to Notice of Special Interest NOT-LM-19-003, we propose novel, domain- independent, reusable causal and statistical methods to address these data-science challenges and to increase the ability of cluster trials to inform clinical and policy decisions by eliminating bias due to selective participation when estimating average treatment effects and when estimating the optimal covariate-dependent treatment strategy. We will evaluate the methods in realistic simulation studies and in empirical analyses using data from 3 large-scale cluster trials of influenza vaccination strategies in U.S. nursing homes.