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Biostatistics Advance Access originally published online on December 14, 2007
Biostatistics 2008 9(3):442-457; doi:10.1093/biostatistics/kxm044
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© The Author 2007. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Monitoring late-onset toxicities in phase I trials using predicted risks

B. Nebiyou Bekele*, Yuan Ji, Yu Shen and Peter F. Thall

Department of Biostatistics, M.D. Anderson Cancer Center, Houston, TX 77030, USA
bbekele{at}mdanderson.org

* To whom correspondence should be addressed.

Late-onset (LO) toxicities are a serious concern in many phase I trials. Since most dose-limiting toxicities occur soon after therapy begins, most dose-finding methods use a binary indicator of toxicity occurring within a short initial time period. If an agent causes LO toxicities, however, an undesirably large number of patients may be treated at toxic doses before any toxicities are observed. A method addressing this problem is the time-to-event continual reassessment method (TITE-CRM, Cheung and Chappell, 2000). We propose a Bayesian dose-finding method similar to the TITE-CRM in which doses are chosen using time-to-toxicity data. The new aspect of our method is a set of rules, based on predictive probabilities, that temporarily suspend accrual if the risk of toxicity at prospective doses for future patients is unacceptably high. If additional follow-up data reduce the predicted risk of toxicity to an acceptable level, then accrual is restarted, and this process may be repeated several times during the trial. A simulation study shows that the proposed method provides a greater degree of safety than the TITE-CRM, while still reliably choosing the preferred dose. This advantage increases with accrual rate, but the price of this additional safety is that the trial takes longer to complete on average.

Keywords: Adaptive design; Bayesian inference; Dose finding; Isotonic regression; Latent variables; Markov chain Monte Carlo; Ordinal modeling; Predictive probability

Received May 14, 2007; revised September 6, 2007; revised October 18, 2007; accepted for publication November 1, 2007.


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