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Evaluation of Community‐Intervention Trials via Generalized Linear Mixed Models

Summary In community‐intervention trials, communities, rather than individuals, are randomized to experimental arms. Generalized linear mixed models offer a flexible parametric framework for the evaluation of community‐intervention trials, incorporating both systematic and random variations at the community and individual levels. We propose here a simple two‐stage inference method for generalized linear mixed models, specifically tailored to the analysis of community‐intervention trials. In the first stage, community‐specific random effects are estimated from individual‐level data, adjusting for the effects of individual‐level covariates. This reduces the model approximately to a linear mixed model with the unit of analysis being community. Because the number of communities is typically small in community‐intervention studies, we apply the small‐sample inference method of Kenward and Roger (1997, Biometrics53, 983–997) to the linear mixed model of second stage. We show by simulation that, under typical settings of community‐intervention studies, the proposed approach improves the inference on the intervention‐effect parameter uniformly over both the linearized mixed‐effect approach and the adaptive Gaussian quadrature approach for generalized linear mixed models. This work is motivated by a series of large randomized trials that test community interventions for promoting cancer preventive lifestyles and behaviors.