263 – Contributed Poster Presentations: Biopharmaceutical Section
A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations in the Presence of a Baseline Covariate
Sungjin Kim
Cedars-Sinai Medical Center
Márcio Augusto Diniz
Cedars-Sinai Medical Center
Mourad Tighiouart
Cedars-Sinai Medical Center
We describe a Bayesian adaptive design for estimating the maximum tolerated dose curve as a function of a baseline covariate using two cytotoxic agents. Parametric models are used to describe the relationship between the doses, baseline covariate, and the probability of dose limiting toxicity (DLT). Trial design proceeds by treating cohorts of two patients simultaneously using escalation with overdose control, where at each stage of the trial, we seek a dose of one agent using the current posterior distribution of the MTD of this agent given the current dose of the other agent and the next patient's baseline covariate value. At the end of the trial, we estimate MTD curves as functions 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 the design that uses the covariate to the one that ignores the baseline characteristic. The methodology is further adapted to the case of a pre-specified discrete set of dose combinations.