A Bayesian Case Study in Oncology Phase I Combination Dose-Finding Using Logistic Regression with Covariates

Abstract

A Bayesian approach to finding the maximum tolerated dose (MTD) is presented. The approach is flexible, allowing inclusion of covariates, and enables transparent dose recommendations based on comprehensive inferential summaries on the probability of dose-limiting toxicities (DLT). A case study is presented for a Phase I combination of two oncology drugs, nilotinib and imatinib. Data obtained and decisions made during the study are described. Final determination of the MTD pair is outlined, along with discussion regarding the use and interpretability of within- and end-of-study data.

Key Words:

• Bayesian inference
• Covariates
• Dose-escalation
• Drug-combination
• Logistic regression
• Maximum tolerated dose
• Oncology

ACKNOWLEDGMENTS

The authors would like to extend their thanks and appreciation to the patients who took part in the described study and the investigators who facilitated this participation, without whom this paper would not be possible. We would also like to thank the following groups and individuals: the AMN107 International Project Team and the study's Clinical Trial Team for providing the data; Novartis Business Unit Oncology's Biostatistics Early Clinical Development (Bios-ECD) team for their helpful comments and continuing support in the development and application of the Bayesian dose-escalation designs; Jeffrey Eisele and William Mietlowski for promoting the Bayesian dose escalation designs at Novartis during their time in Bios-ECD; and Thomas Gsponer for his support of activities relating to this methodology.

Notes

∗Dose-pair does not meet the overdose control criteria.

∗Dose-pair does not meet the overdose control criteria.

∗Dose-pair does not meet the overdose control criteria.