Hierarchical models for sharing information across populations in phase I dose-escalation studies Journal Article

Authors: Cunanan, K. M.; Koopmeiners, J. S.
Article Title: Hierarchical models for sharing information across populations in phase I dose-escalation studies
Abstract: The primary goal of a phase I clinical trial in oncology is to evaluate the safety of a novel treatment and identify the maximum tolerated dose, defined as the maximum dose with a toxicity rate below some pre-specified threshold. Researchers are often interested in evaluating the performance of a novel treatment in multiple patient populations, which may require multiple phase I trials if the treatment is to be used with background standard-of-care that varies by population. An alternate approach is to run parallel trials but combine the data through a hierarchical model that allows for a different maximum tolerated dose in each population but shares information across populations to achieve a more accurate estimate of the maximum tolerated dose. In this manuscript, we discuss hierarchical extensions of three commonly used models for the dose–toxicity relationship in phase I oncology trials. We then propose three dose-finding guidelines for phase I oncology trials using hierarchical modeling. The proposed guidelines allow us to fully realize the benefits of hierarchical modeling while achieving a similar toxicity profile to standard phase I designs. Finally, we evaluate the operating characteristics of a phase I clinical trial using the proposed hierarchical models and dose-finding guidelines by simulation. Our simulation results suggest that incorporating hierarchical modeling in phase I dose-escalation studies will increase the probability of correctly identifying the maximum tolerated dose and the number of patients treated at the maximum tolerated dose, while decreasing the rate of dose-limiting toxicities and number of patients treated above the maximum tolerated dose, in most cases. © The Author(s) 2017.
Keywords: phase i; continual reassessment method; hierarchical modeling; concurrent studies; multiple cancer populations
Journal Title: Statistical Methods in Medical Research
Volume: 27
Issue: 11
ISSN: 0962-2802
Publisher: Sage Publications  
Date Published: 2018-11-01
Start Page: 3447
End Page: 3459
Language: English
DOI: 10.1177/0962280217703812
PUBMED: 28480828
PROVIDER: scopus
Notes: Article -- Export Date: 1 November 2018 -- Source: Scopus
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MSK Authors
  1. Kristen   Cunanan
    13 Cunanan
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