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Biomarker-Driven Adaptive Design

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Nonparametric Bayesian Inference in Biostatistics

Part of the book series: Frontiers in Probability and the Statistical Sciences ((FROPROSTAS))

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Abstract

We review some principles and implementations of Bayesian model-based adaptive enrichment and population finding designs that exploit biomarker information to propose adaptive treatment allocation and recommend patient subpopulations that might most benefit from the treatments under consideration.

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Acknowledgements

Yuan Ji and Peter Müller’s research is partially supported by NIH R01 CA132897.

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Authors and Affiliations

  1. The University of Texas at Austin, 1, University Station, C1200, Austin, TX, 78712, USA

    Yanxun Xu & Peter Müller

  2. NorthShore University HealthSystem/The University of Chicago, 1001 University Place, Evanston, IL, 60201, USA

    Yuan Ji

Authors
  1. Yanxun Xu
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  2. Yuan Ji
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  3. Peter Müller
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Corresponding author

Correspondence to Yanxun Xu .

Editor information

Editors and Affiliations

  1. Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky, USA

    Riten Mitra

  2. Department of Mathematics, University of Texas, Austin, Texas, USA

    Peter Müller

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Xu, Y., Ji, Y., Müller, P. (2015). Biomarker-Driven Adaptive Design. In: Mitra, R., Müller, P. (eds) Nonparametric Bayesian Inference in Biostatistics. Frontiers in Probability and the Statistical Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-19518-6_15

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