title: Proportional odds model for dose-finding clinical trial designs with ordinal toxicity grading.
abstract: Currently many dose-finding clinical trial designs, including the continual reassessment method (CRM) and the standard ' 3 + 3' design, dichotomize toxicity outcomes based on the pre-specified dose-limiting toxicity (DLT) criteria. This loss of information is particularly inefficient due to the small sample sizes in phase I trials. Common Toxicity Criteria (CTCAEv3.0) classify adverse events into grades 1-5, which range from 1 as a mild adverse event to 5 as death related to an adverse event. In this paper, we extend the CRM to include ordinal toxicity outcomes as specified by CTCAEv3.0 using the proportional odds model (POM) and compare results with the dichotomous CRM. A sensitivity analysis of the new design compares various target DLT rates, sample sizes, and cohort sizes. This design is also assessed under various dose-toxicity relationship models including POMs as well as those that violate the proportional odds assumption. A simulation study shows that the proportional odds CRM performs as well as the dichotomous CRM on all criteria compared (including safety criteria such as percentage of patients treated at highly toxic or suboptimal dose levels) and with improved estimation of the maximum tolerated dose when the PO assumption is not violated. These findings suggest that it is beneficial to incorporate ordinal toxicity endpoints into phase I trial designs.
authors.affiliation: Division of Biostatistics and Epidemiology, Medical University of South Carolina, Charleston, SC 29425, USA.
authors.forename: Emily M
authors.initials: EM
authors.lastname: Van Meter
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Garrett-Mayer
authors.forename: Dipankar
authors.initials: D
authors.lastname: Bandyopadhyay
mesh_terms: D017321:Clinical Trials, Phase I as Topic / Q000379:methods*; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D006801:Humans; D020714:Maximum Tolerated Dose; D015233:Models, Statistical; D012107:Research Design