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Novel Bayesian Adaptive Designs and Their Applications in Cancer Clinical Trials

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Computational and Methodological Statistics and Biostatistics

Part of the book series: Emerging Topics in Statistics and Biostatistics ((ETSB))

Abstract

Clinical trial is a prescribed learning process for identifying safe and effective treatments. In recent years, rapid advancements in cancer biology, immunology, genomics, and treatment development have demanded innovative methods to identify better therapies for the most appropriate population in a timely, efficient, accurate, and cost-effective way. In this chapter, we will first illustrate the concept of Bayesian update and Bayesian inference, which is a superior alternative to the traditional frequentist approach. Bayesian methods take the "learn as we go" approach, making them innately suitable for clinical trials. Then, we will give an overview of Bayesian adaptive designs in the areas of adaptive dose finding, posterior probability and predictive probability calculation, outcome adaptive randomization, multi-endpoint phase II design, multi-arm, multi-stage platform design, hierarchical modeling, etc. In particular, a new class of model-assisted designs will be introduced, which combine the transparency and simplicity of conventional algorithm-based designs with the superiority and rigorousness of model-based designs. These designs enjoy superior performance comparable to more complicated, model-based designs, though they are also capable of simplicity similar to conventional designs. Examples of the Bayesian optimal interval (BOIN), the keyboard, the time-to-event BOIN (TITE-BOIN), the BOIN combination, and the Bayesian Optimal Phase 2 (BOP2) designs will be discussed. Real applications, including BATTLE trial in lung cancer, I-SPY 2 trial in breast cancer, and GBM AGILE in glioblastoma, will be given. The chapter will also introduce software tools, including downloadable programs and online Shiny applications for the design and conduct of clinical trials. Bayesian adaptive clinical trial designs increase study efficiency, allow more flexible trial conduct, and treat a greater number of patients with more effective treatments in the trial. They also possess desirable frequentist properties. Useful software tools can be found at: https://biostatistics.mdanderson.org/SoftwareDownload/and https://trialdesign.org/.

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Authors and Affiliations

  1. Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Ruitao Lin & J. Jack Lee

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  1. Ruitao Lin
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Correspondence to J. Jack Lee .

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Editors and Affiliations

  1. Department of Statistics, University of Pretoria, Pretoria, South Africa

    Andriëtte Bekker

  2. Department of Statistics, University of Pretoria, Pretoria, South Africa

    (Din) Ding-Geng Chen

  3. Department of Statistics, University of Pretoria, Pretoria, South Africa

    Johannes T. Ferreira

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Lin, R., Lee, J.J. (2020). Novel Bayesian Adaptive Designs and Their Applications in Cancer Clinical Trials. In: Bekker, A., Chen, (.DG., Ferreira, J.T. (eds) Computational and Methodological Statistics and Biostatistics. Emerging Topics in Statistics and Biostatistics . Springer, Cham. https://doi.org/10.1007/978-3-030-42196-0_17

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