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The Bayesian Design of Adaptive Clinical Trials

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  • Alessandra Giovagnoli

    (Department of Statistical Sciences, University of Bologna, 40126 Bologna, Italy
    Retired.)

Abstract

This paper presents a brief overview of the recent literature on adaptive design of clinical trials from a Bayesian perspective for statistically not so sophisticated readers. Adaptive designs are attracting a keen interest in several disciplines, from a theoretical viewpoint and also—potentially—from a practical one, and Bayesian adaptive designs, in particular, have raised high expectations in clinical trials. The main conceptual tools are highlighted here, with a mention of several trial designs proposed in the literature that use these methods, including some of the registered Bayesian adaptive trials to this date. This review aims at complementing the existing ones on this topic, pointing at further interesting reading material.

Suggested Citation

  • Alessandra Giovagnoli, 2021. "The Bayesian Design of Adaptive Clinical Trials," IJERPH, MDPI, vol. 18(2), pages 1-15, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:2:p:530-:d:477961
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    References listed on IDEAS

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    Cited by:

    1. Bethany Jablonski Horton & Nolan A. Wages & Ryan D. Gentzler, 2021. "Bayesian Design for Identifying Cohort-Specific Optimal Dose Combinations Based on Multiple Endpoints: Application to a Phase I Trial in Non-Small Cell Lung Cancer," IJERPH, MDPI, vol. 18(21), pages 1-10, October.

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