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Evaluation of Community-Intervention Trials via Generalized Linear Mixed Models

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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.

Keywords: Correlated data; Group randomization; Random effects; Small-sample inference

Document Type: Research Article


Affiliations: 1: Cancer Prevention Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N., P.O. Box 19024, Seattle, Washington 98109-1024, U.S.A. 2: Department of Biostatistics, University of Washington, Seattle, Washington 98195, U.S.A. 3: Department of Epidemiology and Biostatistics, University of California, San Francisco, California 94143-0560, U.S.A.

Publication date: December 1, 2004


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