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    Optimizing randomized clinical trial treatment allocation can improve efficiency. This study introduces covariate-dependent allocations for estimating treatment effects in target populations, enhancing generalizability beyond trial participants.

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    Area of Science:

    • Clinical Trials
    • Biostatistics
    • Epidemiology

    Background:

    • Randomized clinical trials (RCTs) frequently use data to estimate treatment effects in target populations distinct from the trial population.
    • Current optimal treatment allocation methods primarily focus on the trial population, potentially limiting generalizability.
    • Differences between trial and target populations necessitate adjustments in treatment allocation strategies.

    Purpose of the Study:

    • To develop optimal treatment allocation mechanisms for RCTs that account for differences between trial and target populations.
    • To provide methods for maximizing nonparametric efficiency bounds when estimating treatment effects in a target population.
    • To explore how covariate distributions in target populations influence optimal treatment allocations.

    Main Methods:

    • Derivation of optimal treatment allocations by maximizing nonparametric efficiency bounds for various treatment effect measures.
    • Consideration of three data configurations: transportation, generalization, and post-stratification.
    • Analysis of how baseline covariates and target population covariate distributions impact allocation strategies.

    Main Results:

    • Optimal treatment allocations can depend on the target population's covariate distribution, not necessarily the data configuration.
    • For general effect measures, allocations are optimized by considering target population covariates.
    • A unique covariate-dependent allocation maximizes efficiency for estimating average treatment effects, irrespective of target distribution or data configuration.

    Conclusions:

    • Optimal treatment allocation in RCTs should consider target population characteristics for improved generalizability.
    • The proposed methods enhance the estimation of treatment effects in populations beyond the trial participants.
    • Covariate-dependent allocations offer a robust strategy for maximizing efficiency in diverse clinical trial settings.