A Bayesian phase I/II design for cancer clinical trials combining an immunotherapeutic agent with a chemotherapeutic agent

Beibei Guo, Elizabeth Garrett-Mayer, Suyu Liu

Research output: Contribution to journalArticlepeer-review

Abstract

Immunotherapy is an innovative treatment approach that harnesses a patient’s immune system to treat cancer. It has provided an alternative and complementary treatment modality to conventional chemotherapy. Combining immunotherapy with cytotoxic chemotherapy agent has become the leading trend and the most active research field in oncology. To accommodate this growing trend, we propose a Bayesian phase I/II dose-finding design to identify the optimal biological dose combination (OBDC), defined as the dose combination with the highest desirability in the risk-benefit trade-off. We propose new statistical models to describe the relationship between the doses and treatment outcomes, including immune response, toxicity and progression-free survival (PFS). During the trial, based on accrued data, we continuously update model estimates and adaptively assign patients to dose combinations with high desirability. The simulation study shows that our design has desirable operating characteristics.

Original languageEnglish (US)
Pages (from-to)1210-1229
Number of pages20
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume70
Issue number5
DOIs
StatePublished - Nov 2021

Keywords

  • Bayesian adaptive design
  • dose-finding
  • drug combination
  • immunotherapy
  • risk-benefit tradeoff

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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