A Bayesian adaptive design for dual-agent phase I–II oncology trials integrating efficacy data across stages

José L. Jiménez, Haiyan Zheng

Research output: Contribution to journalArticlepeer-review


Combination of several anticancer treatments has typically been presumed to have enhanced drug activity. Motivated by a real clinical trial, this paper considers phase I–II dose finding designs for dual-agent combinations, where one main objective is to characterize both the toxicity and efficacy profiles. We propose a two-stage Bayesian adaptive design that accommodates a change of patient population in-between. In stage I, we estimate a maximum tolerated dose combination using the escalation with overdose control (EWOC) principle. This is followed by a stage II, conducted in a new yet relevant patient population, to find the most efficacious dose combination. We implement a robust Bayesian hierarchical random-effects model to allow sharing of information on the efficacy across stages, assuming that the related parameters are either exchangeable or nonexchangeable. Under the assumption of exchangeability, a random-effects distribution is specified for the main effects parameters to capture uncertainty about the between-stage differences. The inclusion of nonexchangeability assumption further enables that the stage-specific efficacy parameters have their own priors. The proposed methodology is assessed with an extensive simulation study. Our results suggest a general improvement of the operating characteristics for the efficacy assessment, under a conservative assumption about the exchangeability of the parameters a priori.

Original languageEnglish
Article number2200288
JournalBiometrical Journal
Issue number7
Early online date18 May 2023
Publication statusPublished - Oct 2023

Bibliographical note

Funding Information:
Dr. Zheng's contribution to this manuscript was supported by Cancer Research UK (RCCPDF\100008).


  • drug combination
  • information borrowing
  • meta-analytic-combined
  • phase I–II
  • seamless designs

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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