Abstract
Observational studies may provide suggestive evidence for the results of behavior change and lifestyle modification, but they do not replace randomized trials for comparing interventions. To obtain a valid comparison of competing intervention strategies, randomized trials of adequate size are the recommended approach. Randomization avoids bias, achieves balance (on average) of both known and unknown predictive factors between intervention and comparison groups, and provides the basis of statistical tests. The value of randomization is as relevant when investigating community interventions as it is for studies that are directed at individuals. Randomization by group is less efficient statistically than randomization by individual, but there are reasons why randomization by group (such as community) may be chosen, including feasibility of delivery of the intervention, political and administrative considerations, avoiding contamination between individuals allocated to competing interventions, and the very nature of the intervention. One example is the Community Intervention Trial for Smoking Cessation (COMMIT), which involved 11 matched pairs of communities and randomized within these pairs to active community-level intervention versus comparison. For analysis of results, community-level permutation tests (and corresponding test-based confidence intervals) can be designed based on the randomization distribution. The advantages of this approach are that it is robust, and the unit of randomization is the unit of analysis, yet it can incorporate individual-level covariates. Such covariates can play a role in imputation for missing values, adjustment for imbalances, and separate analyses in demographic subsets (with appropriate tests for interaction). A communityrandomized trial can investigate a multichannel community-based approach to lifestyle modification, thus providing generalizability coupled with a rigorous evaluation of the intervention.
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