A Bayesian dose-finding design incorporating toxicity data from multiple treatment cycles

Jun Yin, Rui Qin, Monia Ezzalfani, Daniel J. Sargent, Sumithra J. Mandrekar

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Phase I oncology trials are designed to identify a safe dose with an acceptable toxicity profile. The dose is typically determined based on the probability of severe toxicity observed during the first treatment cycle, although patients continue to receive treatment for multiple cycles. In addition, the toxicity data from multiple types and grades are typically summarized into a single binary outcome of dose-limiting toxicity. A novel endpoint, the total toxicity profile, was previously developed to account for the multiple toxicity types and grades. In this work, we propose to account for longitudinal repeated measures of total toxicity profile over multiple treatment cycles, accounting for cumulative toxicity during dosing-finding. A linear mixed model was utilized in the Bayesian framework, with addition of Bayesian risk functions for decision-making in dose assignment. The performance of this design is evaluated using simulation studies and compared with the previously proposed quasi-likelihood continual reassessment method (QLCRM) design. Twelve clinical scenarios incorporating four different locations of maximum tolerated dose and three different time trends (decreasing, increasing, and no effect) were investigated. The proposed repeated measures design was comparable with the QLCRM when only cycle 1 data were utilized in dose-finding; however, it demonstrated an improvement over the QLCRM when data from multiple cycles were used across all scenarios.

Original languageEnglish (US)
Pages (from-to)67-80
Number of pages14
JournalStatistics in Medicine
Issue number1
StatePublished - Jan 15 2017


  • continuous endpoint
  • late and cumulative toxicity
  • longitudinal
  • molecularly targeted agent
  • oncology
  • phase I

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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