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    This study introduces automated patient subgroup detection using model-based recursive partitioning. The method identifies subgroups with differing treatment effects, exemplified by Riluzole in amyotrophic lateral sclerosis patients.

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    Area of Science:

    • Biostatistics
    • Clinical Trial Methodology
    • Pharmacogenomics

    Background:

    • Identifying patient subgroups with differential treatment effects is crucial for personalized medicine.
    • Regulatory bodies like the EMA are developing guidelines for subgroup analyses in clinical trials.
    • Data-driven identification of patient subgroups presents statistical challenges.

    Purpose of the Study:

    • To introduce and validate a statistical procedure for the automated detection of patient subgroups with differential treatment effects.
    • To address the challenges outlined by the EMA regarding data-driven subgroup identification.
    • To apply the method to identify patient subgroups with varying responses to Riluzole in amyotrophic lateral sclerosis.

    Main Methods:

    • Model-based recursive partitioning was employed for automated subgroup detection.
    • The procedure identifies parameter instabilities in treatment effects within a primary analysis model.
    • A decision tree links identified subgroups to predictive factors.

    Main Results:

    • The method successfully produced a segmented model with differential treatment parameters for distinct patient subgroups.
    • Application to amyotrophic lateral sclerosis revealed patient subgroups with differing Riluzole treatment effects.
    • The procedure demonstrated the capability for data-driven identification of clinically relevant subgroups.

    Conclusions:

    • Model-based recursive partitioning offers a robust approach for automated patient subgroup identification.
    • This methodology facilitates the development of individualized treatment strategies.
    • The findings have implications for optimizing drug efficacy in diseases like amyotrophic lateral sclerosis.