SAPA-NE 2023 Bimonthly Seminar

A Hybrid Design for Dose-Finding Oncology Clinical Trials

Abstract

Identifying the maximum tolerated dose (MTD) and recommending a Phase II dose for an investigational treatment is crucial in cancer drug development. A suboptimal dose often leads to a failed late-stage trial, while an overly toxic dose causes harm to patients. There is a very rich literature on trial designs for dose-finding oncology clinical trials. We pro[1]pose a novel hybrid design that maximizes the merits and minimizes the limitations of the existing designs. Building on two existing dose-finding designs: a model-assisted design (the modified toxicity probability interval) and a dose-toxicity model-based design, a hybrid design of the modified toxicity probability interval design and a dose-toxicity model such as the logistic regression model is proposed, incorporating optimal properties from these existing approaches. The performance of the hybrid design was tested in a real trial example and through simulation scenarios. The hybrid design controlled the overdosing toxicity well and led to a recommended dose closer to the true MTD due to its ability to calibrate for an intermediate dose. The robust performance of the proposed hybrid design is illustrated through the real trial dataset and simulations. The simulation results demonstrated that the proposed hybrid design can achieve excellent and robust operating characteristics compared to other existing designs and can be an effective model for determining the MTD and recommended Phase II dose in oncology dose-find[1]ingtrials. For practical feasibility, an R-shiny tool was developed and is freely available to guide clinicians in every step of the dose finding process.

Speaker

Dr. Jason Liao, Associate Vice President, Incyte

Dr. Jason J.Z. Liao is currently Associate Vice President of Biostatistics at Incyte Corporation overseeing over 60 studies including Phases 1/2/3, and is the head of biostatistics for early oncology development. After receiving his Ph.D. in Statistics from The University of Michigan, Dr. Liao has worked in the pharmaceutical industry for over 20 years in different therapeutic areas, about 15 years of which were spent at Merck. He has unique knowledge and experience in different stages of drug development for small molecule drugs, large molecular drugs, vaccines and biosimilars, which enables him efficiently to identify the key end-to-end items. Dr. Liao is also very active in promoting and developing fit-4-purpose and/or more powerful statistical methods and efficient study designs for the problems and applications in drug development. Professionally, Dr. Liao is a Fellow of The American Statistical Association.