Bayesian Optimization Phase I Design of Experiment Models

Abstract

This paper offers a concise overview of a novel model for clinical trials, focusing on measurable phase I outcomes from a Bayesian perspective. It outlines hypothetical Bayesian criteria standards and discusses utilization model techniques, including sampler Bayesian models. Bayesian methodologies are increasingly popular in clinical research for their ability to incorporate prior information and adapt trial designs based on accumulating data. Phase I trials are vital for assessing new treatment safety, making them ideal for Bayesian approaches. The model leverages Bayesian principles to guide trial decisions, like dose escalation and maximum tolerated dose determination. By merging prior knowledge with observed data, Bayesian methods provide a framework for informed decisions, especially in scenarios with small sample sizes or historical data. Additionally, the paper explores various Bayesian model techniques, including samplers for posterior inference, enhancing decision-making in clinical trials. Overall, it contributes to Bayesian methodologies by outlining a tailored model for phase I trials and offering practical implementation guidance to improve early-phase trial efficiency and reliability.