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    Area of Science:

    • Statistics
    • Biostatistics
    • Data Science

    Background:

    • Multiple hypothesis testing is crucial for analyzing complex datasets.
    • Correlated clustered data presents unique challenges for standard statistical methods.
    • Existing methods based on maximum likelihood estimation can be computationally demanding.

    Purpose of the Study:

    • To propose a computationally convenient multiple comparison procedure for correlated clustered data.
    • To develop a method that accounts for intra-cluster correlation.
    • To evaluate the performance of the proposed method against existing approaches.

    Main Methods:

    • Utilized the composite likelihood method for constructing test statistics.
    • Developed new test statistics that incorporate the correlation structure within clusters.
    • Conducted simulation studies to assess performance under various correlation scenarios.

    Main Results:

    • The composite likelihood-based procedures demonstrated effective control of the familywise type I error rate.
    • Ignoring intra-cluster correlation led to unstable and erratic performance in simulations.
    • The proposed method is computationally more convenient than maximum likelihood-based procedures.

    Conclusions:

    • Composite likelihood provides a computationally efficient and statistically sound approach for multiple hypothesis testing with correlated clustered data.
    • Accounting for intra-cluster correlation is essential for reliable results.
    • The proposed method offers a practical alternative for researchers dealing with such data structures.