An Adaptive Bayesian Design for Personalized Dosing in a Cancer Prevention Trial.

ABSTRACT:

Introduction

In biomarker-driven clinical trials, translational strategies typically involve moving findings from animal experiments to human trials. Typically, the translation is static, using a fixed model derived from animal experiments for the duration of the trial. Bayesian designs, capable of incorporating information external to the experiment, provide a dynamic translational strategy. This article demonstrates an example of such a dynamic Bayesian strategy in a clinical trial.

Methods

This study explored the effect of a personalized dose of fish oil for reducing prostaglandin E₂, an inflammatory marker linked to colorectal cancer. A Bayesian design was implemented for the dose-finding algorithm that adaptively updated a dose-response model derived from a p

SUBMITTER: Sen A 

PROVIDER: S-EPMC7531353 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature