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    Area of Science:

    • Biostatistics
    • Medical Research Methodology

    Background:

    • Meta-analysis commonly faces the challenge of testing equality for multiple correlated effect sizes across independent studies.
    • This issue is particularly relevant when comparing treatment to control groups.
    • Two practical scenarios involve studies with identical outcome variables or those with missing variables.

    Purpose of the Study:

    • To investigate methods for testing the equality of p correlated effect sizes in meta-analysis.
    • To address scenarios with complete or missing outcome variables across studies.

    Main Methods:

    • Utilizes Hotelling's generalized T-squared statistic for hypothesis testing.
    • Applies the method to scenarios involving correlated effect sizes from independent studies.

    Main Results:

    • The proposed method using Hotelling's generalized T-squared statistic is effective for testing equality of correlated effect sizes.
    • The procedure accommodates both complete and incomplete outcome variable sets across studies.

    Conclusions:

    • Hotelling's generalized T-squared statistic provides a robust approach for meta-analysis of correlated effect sizes.
    • The method is applicable and illustrated with an example for practical use in biostatistics and research.