Extending Inferences from a Cluster‐Randomized Trial to a Target Population

Research Objective In cluster‐randomized trials, participating clusters are not necessarily representative of the target population of trial‐eligible clusters where the experimental treatments might be applied. To the extent that participating and nonparticipating clusters differ in terms of covariates that are modifiers of the treatment effect, the average treatment effect in the trial may not apply to the entire target population. We describe analyses that extend (generalize or transport) causal inferences from cluster‐randomized trials to a target population of clusters, under a general nonparametric model that allows for arbitrary within‐cluster dependence. Study Design We consider study designs where a subset of a cohort of clusters are invited and agree to participate in a randomized trial with cluster‐level treatment assignment (ie, nested trial designs). Treatment and outcome data need only be collected from clusters participating in the trial, but data on baseline covariates must be collected from the entire cohort. We propose doubly robust estimators of potential outcome means in the target population that exploit individual‐level data on covariates and outcomes to improve efficiency and are appropriate for use with machine learning methods. Population Studied We illustrate the methods using a 2 × 2 factorial cluster‐randomized trial of influenza vaccination strategies conducted in 818 nursing homes nested in a cohort of 4,475 trial‐eligible Medicare‐certified nursing homes. To be eligible for the trial, nursing homes had to be free‐standing facilities (ie, not hospital based) within 50 miles of a CDC‐reporting city, have a minimum of 50 long‐stay residents, and at least 80% of residents aged 65 years or older. Principal Findings We applied the methods to estimate the potential outcome means for the four vaccination strategies in the target population of trial‐eligible nursing homes. We estimated the conditional probability of trial participation (nursing home level) and the conditional probability of death from any cause (individual level, separately for each treatment group) using different methods to illustrate that our approach can easily accommodate them. One set of analyses used logistic regression models estimated by maximum likelihood methods. For comparison, we also estimated probabilities using various machine learning methods. Overall, the doubly robust estimator produced similar point estimates for the potential outcome mean under each treatment across all approaches for estimating the conditional probabilities of trial participation and death. Point estimates for generalizability analyses were largely similar to those from the randomized trial, suggesting that the trial‐only estimates were applicable to the broader population of trial‐eligible nursing homes for the outcome of all‐cause death. Conclusions Recent work on extending causal inferences from individually randomized trials to a target population can be extended to the cluster trial setting, under a general causal model that allows for arbitrary within‐cluster dependence. Implications for Policy or Practice When cluster‐randomized trials are embedded in large health care systems, investigators can use the methods we describe to extend causal inferences from the trials to the broader health care system. Primary Funding Source Patient‐Centered Outcomes Research Institute.