![IconGems-Print](images/IconGems-Print.png)
243 – Contributed Poster Presentations: Biopharmaceutical Section
A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations with Ordinal Toxicity Grades
Marcio A. Diniz
Cedars-Sinai Medical Center
Sungjin Kim
Cedars-Sinai Medical Center
Mourad Tighiouart
Cedars-Sinai Medical Center
We propose a Bayesian adaptive design for early phase drug combination cancer trials incorporating ordinal grade of toxicities. Parametric models are used to describe the relationship between the dose combinations and the probabilities of the ordinal toxicities under the proportional odds assumption. Trial design proceeds by treating cohorts of two patients simultaneously using Escalation With Overdose Control (EWOC) and Continual Reassessment Method (CRM). At the end of the trial, we estimate the MTD curves as a function of Bayes estimates of the model parameters. We evaluate design operating characteristics in terms of safety of the trial and percent of dose recommendation at dose combination neighborhoods around the true MTD by comparing this design to the one that uses a binary indicator of DLT.