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    Area of Science:

    • Biostatistics
    • Clinical Trial Methodology
    • Regulatory Science

    Background:

    • Surrogate endpoints are vital for the FDA Accelerated Approval pathway, substituting direct clinical benefit measures.
    • Valid surrogate endpoints require I-association and T-association, but T-association is often overlooked due to methodological gaps.
    • Failure to demonstrate T-association prevents biomarker use in accelerated approvals.

    Purpose of the Study:

    • To introduce a novel statistical method for rigorously assessing T-association.
    • To align biomarker validation with FDA guidelines for accelerated drug approval.
    • To provide a statistical foundation for future accelerated approvals.

    Main Methods:

    • Proposes a method assuming bivariate normal distribution for treatment effects on surrogate and true endpoints.
    • Accounts for both within-study and between-study variances.
    • Estimates key parameters, including the correlation coefficient, using maximum likelihood, restricted maximum likelihood, and Bayesian approaches.

    Main Results:

    • The proposed method rigorously assesses T-association, addressing a critical gap in biomarker validation.
    • Demonstrated utility through simulations and real-world data analysis.
    • Provides a quantifiable metric (correlation coefficient) for the relationship between treatment effects.

    Conclusions:

    • The developed method offers a robust statistical framework for evaluating T-association.
    • Facilitates compliance with FDA guidelines for surrogate endpoint validation.
    • Supports the accelerated approval pathway by ensuring reliable prediction of clinical benefit.