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

Kristen M. Cunanan, Joseph S. Koopmeiners

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)3447-3459
Number of pages13
JournalStatistical methods in medical research
Issue number11
StatePublished - Nov 1 2018

Bibliographical note

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is partially funded by the Doctoral Dissertation Fellowship from the Graduate School at the University of Minnesota (KMC) and a gift from Medtronic Inc. (JSK).

Publisher Copyright:
© The Author(s) 2017.


  • Concurrent studies
  • continual reassessment method
  • hierarchical modeling
  • multiple cancer populations
  • phase I

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