The Annals of Applied Statistics

Bayesian data augmentation dose finding with continual reassessment method and delayed toxicity

Suyu Liu, Guosheng Yin, and Ying Yuan

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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.

Article information

Source
Ann. Appl. Stat., Volume 7, Number 4 (2013), 2138-2156.

Dates
First available in Project Euclid: 23 December 2013

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1387823313

Digital Object Identifier
doi:10.1214/13-AOAS661

Mathematical Reviews number (MathSciNet)
MR3161716

Zentralblatt MATH identifier
1283.62053

Keywords
Bayesian adaptive design late-onset toxicity nonignorable missing data phase I clinical trial

Citation

Liu, Suyu; Yin, Guosheng; Yuan, Ying. Bayesian data augmentation dose finding with continual reassessment method and delayed toxicity. Ann. Appl. Stat. 7 (2013), no. 4, 2138--2156. doi:10.1214/13-AOAS661. https://projecteuclid.org/euclid.aoas/1387823313


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