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Statistical Considerations in Setting Quality Specification Limits Using Quality Data Read chapter Read first chapter

2019 | OriginalPaper | Chapter

A Novel Framework for Bayesian Response-Adaptive Randomization

Authors : Jian Zhu, Ina Jazić, Yi Liu

Published in: Pharmaceutical Statistics

Publisher: Springer International Publishing

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Abstract

The development of response-adaptive randomization (RAR) has taken many different paths over the past few decades. Some RAR schemes optimize certain criteria, but may be complicated and often rely on asymptotic arguments, which may not be suitable in trials with small sample sizes. Some Bayesian RAR schemes are very intuitive and easy to implement, but may not always be tailored toward the study goals. To bridge the gap between these methods, we proposed a framework in which easy-to-implement Bayesian RAR schemes can be derived to target the study goals. We showed that the popular Bayesian RAR scheme that assigns more patients to better performing arms fits in the new framework given a specific intention. We also illustrated the new framework in the setting where multiple treatment arms are compared to a concurrent control arm. Through simulation, we demonstrated that the RAR schemes developed under the new framework outperform a popular method in achieving the pre-specified study goals.

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previous chapter Exact Inference for Adaptive Group Sequential Designs
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Metadata
Title
A Novel Framework for Bayesian Response-Adaptive Randomization
Authors
Jian Zhu
Ina Jazić
Yi Liu
Copyright Year
2019
Book
Pharmaceutical Statistics

Print ISBN: 978-3-319-67385-1

Electronic ISBN: 978-3-319-67386-8

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-67386-8_11

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