title: Adaptive dose modification for phase I clinical trials.
abstract: Most phase I dose-finding methods in oncology aim to find the maximum-tolerated dose from a set of prespecified doses. However, in practice, because of a lack of understanding of the true dose-toxicity relationship, it is likely that none of these prespecified doses are equal or reasonably close to the true maximum-tolerated dose. To handle this issue, we propose an adaptive dose modification (ADM) method that can be coupled with any existing dose-finding method to adaptively modify the dose, when it is needed, during the course of dose finding. To reflect clinical practice, we divide the toxicity probability into three regions: underdosing, acceptable, and overdosing regions. We adaptively add a new dose whenever the observed data suggest that none of the investigational doses are likely to be located in the acceptable region. The new dose is estimated via a nonparametric dose-toxicity model based on local polynomial regression. The simulation study shows that ADM substantially outperforms the similar existing method. We applied ADM to a phase I cancer trial. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Department of Biostatistics, The University of Texas School of Public Health, Houston, 77030, TX, U.S.A.
authors.forename: Yiyi
authors.initials: Y
authors.lastname: Chu
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, 77030, TX, U.S.A.
authors.forename: Haitao
authors.initials: H
authors.lastname: Pan
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, 77030, TX, U.S.A.
authors.forename: Ying
authors.initials: Y
authors.lastname: Yuan
mesh_terms: D017321:Clinical Trials, Phase I as Topic; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D006801:Humans; D020714:Maximum Tolerated Dose; D008495:Medical Oncology; D009369:Neoplasms / Q000188:drug therapy
keywords: MTD; dose finding; dose modification; nonparametric estimation; phase I trials