Bridging across patient subgroups in phase I oncology trials that incorporate animal data

Haiyan Zheng, Lisa V. Hampson, Thomas Jaki

Research output: Contribution to journalArticlepeer-review

2 Citations (SciVal)

Abstract

In this paper, we develop a general Bayesian hierarchical model for bridging across patient subgroups in phase I oncology trials, for which preliminary information about the dose–toxicity relationship can be drawn from animal studies. Parameters that re-scale the doses to adjust for intrinsic differences in toxicity, either between animals and humans or between human subgroups, are introduced to each dose–toxicity model. Appropriate priors are specified for these scaling parameters, which capture the magnitude of uncertainty surrounding the animal-to-human translation and bridging assumption. After mapping data onto a common, ‘average’ human dosing scale, human dose–toxicity parameters are assumed to be exchangeable either with the standardised, animal study-specific parameters, or between themselves across human subgroups. Random-effects distributions are distinguished by different covariance matrices that reflect the between-study heterogeneity in animals and humans. Possibility of non-exchangeability is allowed to avoid inferences for extreme subgroups being overly influenced by their complementary data. We illustrate the proposed approach with hypothetical examples, and use simulation to compare the operating characteristics of trials analysed using our Bayesian model with several alternatives. Numerical results show that the proposed approach yields robust inferences, even when data from multiple sources are inconsistent and/or the bridging assumptions are incorrect.

Original languageEnglish
Pages (from-to)1057-1071
Number of pages15
JournalStatistical Methods in Medical Research
Volume30
Issue number4
Early online date27 Jan 2021
DOIs
Publication statusPublished - 21 Apr 2021

Bibliographical note

Funding Information:
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 633567. Dr Hampson’s contribution to this manuscript was supported by the UK Medical Research Council (grant MR/M013510/1). This report is independent research arising in part from Prof Jaki’s Senior Research Fellowship (NIHR-SRF-2015-08-001) supported by the National Institute for Health Research. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care (DHSC). T Jaki also received funding from the UK Medical Research Council (MC__UU__0002/14).

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This project has received funding from the European Union?s Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No 633567. Dr Hampson?s contribution to this manuscript was supported by the UK Medical Research Council (grant MR/M013510/1). This report is independent research arising in part from Prof Jaki?s Senior Research Fellowship (NIHR-SRF-2015-08-001) supported by the National Institute for Health Research. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care (DHSC). T Jaki also received funding from the UK Medical Research Council (MC__UU__0002/14).

Publisher Copyright:
© The Author(s) 2021.

Keywords

  • Bayesian hierarchical models
  • bridging
  • historical data
  • phase I clinical trials
  • robustness

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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