American Statistical Association
In phase I clinical trials with cytostatic agents, toxicity endpoints, as well as efficacy effects, should be taken into consideration for identifying the optimal biological dose (OBD). Toward this goal, we develop a two-stage Bayesian mixture modeling approach, which first locates the maximum tolerated dose (MTD) through a mixture of parametric and nonparametric models, and then determines the most efficacious dose using Bayesian adaptive randomization among multiple candidate models. In the first stage searching for the MTD, we propose using a beta-binomial model in conjunction with a probit model as a mixture modeling approach, and make decisions based on the model that better fits the toxicity data. The model fitting adequacy is measured by the deviance information criterion and the posterior model probability. In the second stage searching for the OBD, we do not assume that efficacy monotonically increases with the dose and, instead, we enumerate all the possibilities that each dose could have the highest efficacy effect so that the dose-efficacy curve can be increasing, decreasing, or umbrella-shape. We conduct extensive simulation studies to show the advantages of the proposed mixture modeling approach for pinpointing the MTD and OBD, and demonstrate its satisfactory performance with cytostatic agents.
|Date:||Wednesday, September 18, 2013|
|Time:||4:00 - 5:00 P.M.|
Memorial Sloan-Kettering Cancer Center
Department of Epidemiology and Biostatistics
307 East 63rd Street
(between First and Second Avenues)
3rd Floor Conference Room
New York, New York
Note: To gain access to the building, please follow the directions by the telephone in the foyer.