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December 2013 Bayesian data augmentation dose finding with continual reassessment method and delayed toxicity
Suyu Liu, Guosheng Yin, Ying Yuan
Ann. Appl. Stat. 7(4): 2138-2156 (December 2013). DOI: 10.1214/13-AOAS661

Abstract

A major practical impediment when implementing adaptive dose-finding designs is that the toxicity outcome used by the decision rules may not be observed shortly after the initiation of the treatment. To address this issue, we propose the data augmentation continual reassessment method (DA-CRM) for dose finding. By naturally treating the unobserved toxicities as missing data, we show that such missing data are nonignorable in the sense that the missingness depends on the unobserved outcomes. The Bayesian data augmentation approach is used to sample both the missing data and model parameters from their posterior full conditional distributions. We evaluate the performance of the DA-CRM through extensive simulation studies and also compare it with other existing methods. The results show that the proposed design satisfactorily resolves the issues related to late-onset toxicities and possesses desirable operating characteristics: treating patients more safely and also selecting the maximum tolerated dose with a higher probability. The new DA-CRM is illustrated with two phase I cancer clinical trials.

Citation

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Suyu Liu. Guosheng Yin. Ying Yuan. "Bayesian data augmentation dose finding with continual reassessment method and delayed toxicity." Ann. Appl. Stat. 7 (4) 2138 - 2156, December 2013. https://doi.org/10.1214/13-AOAS661

Information

Published: December 2013
First available in Project Euclid: 23 December 2013

zbMATH: 1283.62053
MathSciNet: MR3161716
Digital Object Identifier: 10.1214/13-AOAS661

Rights: Copyright © 2013 Institute of Mathematical Statistics

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Vol.7 • No. 4 • December 2013
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