Literature DB >> 10709804

A comparison of urn designs for randomized clinical trials of K > 2 treatments.

A Ivanova1, W F Rosenberger.   

Abstract

Response-adaptive designs in clinical trials involve incorporating accruing information from patient responses to treatment into the randomization scheme in order to assign more patients to the treatment that has performed better in the trial up to that point. One probability model useful in generating an adaptive randomization scheme is an urn model. We will give a short overview of such adaptive models and compare four of them. We will be interested in how these four models minimize the number of treatment failures in a clinical trial with dichotomous response treatments. Comparison will be done via simulations for four treatments and exactly for three treatments for moderate sample sizes. We compare designs under the assumption that the results of treatments are known immediately, and we also allow some delay in response. Power is analyzed under various alternatives. Our results indicate that a birth and death urn with immigration is the best unless success probabilities are very small, in which case a randomized version of Polya's urn is preferred.

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Year:  2000        PMID: 10709804     DOI: 10.1081/BIP-100101016

Source DB:  PubMed          Journal:  J Biopharm Stat        ISSN: 1054-3406            Impact factor:   1.051


  4 in total

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Journal:  J Stat Plan Inference       Date:  2016-10-07       Impact factor: 1.111

2.  Response adaptive randomization procedures in seamless phase II/III clinical trials.

Authors:  Hongjian Zhu; Jin Piao; J Jack Lee; Feifang Hu; Lixin Zhang
Journal:  J Biopharm Stat       Date:  2019-08-27       Impact factor: 1.051

3.  Generalisations of a Bayesian decision-theoretic randomisation procedure and the impact of delayed responses.

Authors:  S Faye Williamson; Peter Jacko; Thomas Jaki
Journal:  Comput Stat Data Anal       Date:  2022-10       Impact factor: 2.035

4.  Response-adaptive designs for binary responses: How to offer patient benefit while being robust to time trends?

Authors:  Sofía S Villar; Jack Bowden; James Wason
Journal:  Pharm Stat       Date:  2017-12-19       Impact factor: 1.894

  4 in total

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