Bayesian two‐stage dose finding for cytostatic agents via model adaptation. Issue 3 (12th January 2016)

• Record Type: Journal Article
• Authors: Xu, Jiajing; Yin, Guosheng; Ohlssen, David; Bretz, Frank
• Is Part Of: Journal of the Royal Statistical Society. Volume 65:Issue 3(2016:May)
• Page Start: 465
• Page End: 482
• Publication Date: 2016-01-12
• Subjects: Bayesian adaptive design -- Cytostatic agent -- Deviance information criterion -- Dose finding -- Efficacy -- Model selection -- Probit model -- Toxicity; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12129
• Languages: English
• ISSNs: 0035-9254

Abstract

Summary: In phase I clinical trials with cytostatic agents, the typical objective is to identify the optimal biological dose, which should be tolerable as well as achieving the highest effectiveness. Towards this goal, we consider binary toxicity and efficacy end points simultaneously and develop a two‐stage Bayesian adaptive design. Stage 1 searches for the maximum tolerated dose by using a beta–binomial model in conjunction with a probit model, for which decision making is based on the model that fits the toxicity data better. Stage 2 identifies the optimal biological dose while still controlling the level of toxicity. We enumerate all the possibilities that each of the admissible doses may deliver the highest effectiveness so that the dose–efficacy curve is allowed to be increasing, decreasing or concave. We conduct simulation studies to examine the ability of the proposed method to pinpoint both the maximum tolerated dose and the optimal biological dose and demonstrate the design's satisfactory performance with the BKM120 and cetuximab phase I clinical trials.