Evaluation of community-intervention trials via generalized linear mixed models.
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.
Yasui
Yutaka
Y
Cancer Prevention Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N., P.O. Box 19024, Seattle, Washington 98109-1024, USA. yyasui@ualberta.ca
Feng
Ziding
Z
Diehr
Paula
P
McLerran
Dale
D
Beresford
Shirley A A
SA
McCulloch
Charles E
CE
D001699:Biometry; D003153:Community Health Services / Q000706:statistics & numerical data; D003159:Community-Institutional Relations; D003430:Cross-Sectional Studies; D003625:Data Collection; D004032:Diet; D005638:Fruit; D006293:Health Promotion / Q000706:statistics & numerical data; D006801:Humans; D016014:Linear Models; D011634:Public Health; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D014675:Vegetables; D014861:Washington