Ruitao Lin, Assistant Professor of Biostatistics at the University of Texas MD Anderson Cancer Center, will present:

“DEMO: Bayesian Adaptive Dose Exploration-Monitoring-Optimization Design Based on Short, Intermediate, and Long-term Outcomes”

The advancements in targeted therapy or immunotherapy have challenged the conventional “more-is-better” paradigm in oncology. Due to the deadly nature of cancer, an optimal dose recommended from early-phase trials should ultimately possess a promising long-term survival profile. Conventional early-phase trial designs that rely on short-term toxicity or efficacy data may identify a dose with suboptimal long-term benefits. Moreover, many existing designs fail to account for short-term pharmacodynamics when making decisions. Following the guideline of Project Optimus, a generalized dose optimization procedure consisting of three seamlessly connected stages is proposed. Throughout the three stages, the proposed design employs various endpoints, ranging from short-term to long-term, to make informed decisions. In the first dose-exploration stage, short-term toxicity and pharmacodynamics data are utilized to timely screen out doses that are overly toxic or biologically inactive. In the subsequent stages, patients are randomized to admissible doses, which are further monitored based on intermediate outcomes such as toxicity and tumor responses. At the end of the trial, an optimal dose is determined through maximizing the restricted mean survival time. Results from simulation studies indicate that the proposed design can identify a desired dose that is favorable in terms of toxicity, biological activity, tumor response, and survival benefits. Sensitivity analyses indicate that the design is robust to changes in prior specifications and model misspecifications.

A seminar tea will be held at 11:00 a.m. in University Office Plaza, Room 240. All are Welcome.