Meizi Liu, University of Chicago

• Title: PoD-BIN: A Probability of Decision Bayesian Interval De- sign for Time-to-Event Dose-Finding Trials with Multiple Toxicity Grades

• Abstract: We consider a Bayesian framework based on “probability of decision” for dose- finding trial designs. The proposed PoD-BIN design evaluates the posterior predictive probabilities of up-and-down decisions. In PoD-BIN, multiple grades of toxicity, catego- rized as mild toxicity (MT) and dose-limiting toxicity (DLT), are modeled simultaneously, and the primary outcome of interests is time-to-toxicity for both MT and DLT. This allows the possibility of enrolling new patients when previously enrolled patients are still being fol- lowed for toxicity, thus potentially shortening the trial length. The Bayesian decision rules in PoD-BIN utilize the probability of decisions to balance the trade-off between the need to speed up the trial and the risk of exposing patients to overly toxic doses. We demonstrate via numerical examples the resulting trade-off of speed and safety of PoD-BIN and com- pare it to existing designs. PoD-BIN appears to be able to control the frequency of making risky decisions and, at the same time, shorten the trial duration in the simulation.