• Am J Prev Med · Oct 2020

    Clinical Trial

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

    • Ananda Sen, Lili Zhao, Zora Djuric, D Kim Turgeon, Mack T Ruffin, William L Smith, Dean E Brenner, and Daniel P Normolle.
    • Department of Family Medicine, University of Michigan Medical School, Ann Arbor, Michigan; Department of Biostatistics, University of Michigan Medical School, Ann Arbor, Michigan. Electronic address: anandas@umich.edu.
    • Am J Prev Med. 2020 Oct 1; 59 (4): e167-e173.

    IntroductionIn 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.MethodsThis 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 previously completed animal study during the clinical trial. In the initial stages of the trial, the dose-response model parameters were estimated from the rodent data. The model was updated following a Bayesian algorithm after data on every 10‒15 subjects were obtained until the model stabilized. Subjects were enrolled in the study between 2013 and 2015, and the data analysis was carried out in 2016.ResultsThe 3 dosing models were used for groups of 16, 15, and 15 subjects. The mean target dose significantly decreased from 6.63 g/day (Model 1) to 4.06 g/day (Model 3) (p=0.001). Compared with the static strategy of dosing with a single model, the dynamic modeling reduced the dose significantly by about 1.38 g/day on average.ConclusionsA Bayesian design was effective in adaptively revising the dosing algorithm, resulting in a lower pill burden.Trial RegistrationThis study is registered at www.clinicaltrials.gov NCT01860352.Copyright © 2020 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

      Pubmed     Full text   Copy Citation     Plaintext  

      Add institutional full text...

    Notes

     
    Knowledge, pearl, summary or comment to share?
    300 characters remaining
    help        
    You can also include formatting, links, images and footnotes in your notes
    • Simple formatting can be added to notes, such as *italics*, _underline_ or **bold**.
    • Superscript can be denoted by <sup>text</sup> and subscript <sub>text</sub>.
    • Numbered or bulleted lists can be created using either numbered lines 1. 2. 3., hyphens - or asterisks *.
    • Links can be included with: [my link to pubmed](http://pubmed.com)
    • Images can be included with: ![alt text](https://bestmedicaljournal.com/study_graph.jpg "Image Title Text")
    • For footnotes use [^1](This is a footnote.) inline.
    • Or use an inline reference [^1] to refer to a longer footnote elseweher in the document [^1]: This is a long footnote..

    hide…