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Modified Bayes technique in sequential clinical trials.

O E Percus, J K Percus

    Computers in Biology and Medicine
    |January 1, 1984
    PubMed
    Summary
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    Optimizing drug treatment involves balancing patient needs with learning drug efficacy. A biased approach improves overall population outcomes and avoids favoring less effective drugs.

    Area of Science:

    • Biostatistics
    • Clinical Trial Design
    • Pharmacoeconomics

    Background:

    • Optimizing treatment selection for two drugs with unknown efficacy is crucial.
    • Information from patient outcomes must inform both current and future treatment decisions.
    • The goal is to identify the superior drug while treating patients effectively.

    Purpose of the Study:

    • To develop and evaluate a method for optimizing two-drug treatment strategies.
    • To ensure that treatment converges towards the more effective drug over time.
    • To compare the proposed method with existing sampling-plus-stopping-rule techniques.

    Main Methods:

    • Utilizing a straightforward Bayes estimator to update drug efficacy probabilities.
    • Employing computer simulations and algebraic analysis to assess estimator performance.

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  • Introducing a bias towards success in the prior distribution of success probabilities.

    • Main Results:

    • A simple Bayes estimator can lead to 'trapping' with the poorer drug due to early poor results from the better drug.
    • Imposing a bias towards success in the prior distribution mitigates the trapping issue.
    • The proposed biased approach is ethically sound for individual patients.

    Conclusions:

    • The developed biased Bayes estimator protocol is effective in optimizing two-drug treatment.
    • This method ethically prioritizes individual patient well-being while improving population-level outcomes.
    • The protocol outperforms certain sampling-plus-stopping-rule techniques in comparative analyses.