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    Multilevel models (MLMs) are essential for analyzing group-randomized behavioral interventions. These models correctly handle non-independent data, providing accurate results for continuous and dichotomous outcomes.

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    Area of Science:

    • Behavioral Science
    • Biostatistics
    • Public Health

    Background:

    • Group-randomized interventions are common in large-scale behavioral research.
    • Ignoring the nested structure of data (individuals within groups) leads to inaccurate statistical analysis.
    • Traditional methods can yield inefficient parameter estimates and biased test statistics.

    Purpose of the Study:

    • To explain the necessity of multilevel models (MLMs) for group-randomized designs.
    • To demonstrate the application of MLMs using data from the Safer Choices study.
    • To guide researchers in selecting appropriate analytical models for nested data.

    Main Methods:

    • Application of multilevel models (MLMs) for statistical analysis.
    • Utilizing data from the Safer Choices study for illustration.
    • Analysis of both continuous and dichotomous outcome variables.

    Main Results:

    • Multilevel models (MLMs) provide a flexible and appropriate approach for analyzing nested data structures common in group-randomized trials.
    • Demonstrated successful application of MLMs for both continuous and dichotomous outcomes in the Safer Choices study.
    • Highlighting the importance of considering MLM features during study design.

    Conclusions:

    • Multilevel models (MLMs) are crucial for accurate analysis of group-randomized behavioral interventions.
    • Researchers must account for data non-independence using appropriate statistical techniques like MLMs.
    • Proper analytical model selection is vital for valid program effect evaluation in nested study designs.