Bayesian Design Study of the TGN1412 Trial

Maria Daya Roopa; Nimitha John

doi:10.17485/IJST/v16i3.1554

Article

Bayesian Design Study of the TGN1412 Trial

VIEWS 744
PDF 181

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i3.1554

Year: 2023, Volume: 16, Issue: 3, Pages: 223-229

Original Article

Bayesian Design Study of the TGN1412 Trial

Maria Daya Roopa^1*, Nimitha John¹

¹Department of Statistics, Christ University, Hosur Road, Bengaluru, 560029, Karnataka, India

*Corresponding Author
Email: [email protected]

Received Date:30 July 2022, Accepted Date:24 December 2022, Published Date:23 January 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: This study considers the Bayesian design of clinical trials from the perspective of a comparative study of the Bayesian approach for the TGN1412 clinical trial. The Bayesian setting analyses the statistical issues of the TGN1412 trials’ first in man studies, which include the details of the Northwick Park trial, and the clinical study design recommendations are implemented. Methods: The clinical trial data analysis was carried out to screen the structural evidence of the trial outcomes. Hierarchical modeling is implemented with structural priors to get the most efficient Bayesian outcomes. Findings: We concentrated on determining the best model for the clinical pathway of randomization and Bayesian design of experiments. Novelty: The Bayesian design of experiments is widely used for outcome prediction under various treatments in order to validate the trial’s dosing periods. The purpose of this paper is to compare the growth models for Phase I trial results. Current research on Bayesian designs incorporates simulation studies on the effects of design variables.

Keywords: Clinical Trial; Bayesian; Efficacy; Dose Limiting Event; Desirable Outcome; Treatment Effects; Cohort Effects; Dose Response

References

Brush J, Lee M, Sherbino J, Taylor-Fishwick JC, Norman G. Effect of Teaching Bayesian Methods Using Learning by Concept vs Learning by Example on Medical Students' Ability to Estimate Probability of a Diagnosis: A Randomized Clinical Trial. 2019. Available from: https://doi.org/10.1001/jamanetworkopen.2019.18023
Lin J, Lin J. Incorporating propensity scores for evidence synthesis under bayesian framework: review and recommendations for clinical studies. Journal of Biopharmaceutical Statistics. 2022;32(1):53–74. Available from: https://doi.org/10.1080/10543406.2021.1882481
Halpern SD, Chowdhury M, Bayes B, Cooney E, Hitsman BL, Schnoll RA, et al. The Effectiveness and Ethics of Incentives for Research Participation: Two Embedded Randomized Recruitment Trials. SSRN Electronic Journal. 2021;181(11):1479–1488. Available from: https://doi.org/10.1001/jamainternmed.2021.5450
Adler N. Research network on socioeconomic status and health. The Macarthur Network. Available from: https://www.macfound.org/networks/research-network-on-socioeconomic-status-health.DOI
Barton-Hulsey A, Sterling A. Grammatical judgement and production in male 685 participants. Clinical Linguistics and Phonetics. 2020;34(12). Available from: https://doi.org/10.1080/02699206.2020.1719208
Brydges CR, Gaeta L. An Analysis of Nonsignificant Results in Audiology Using Bayes Factors. Journal of Speech, Language, and Hearing Research. 2019;62(12):4544–4553. Available from: https://doi.org/10.1044/2019_JSLHR-H-19-0182

Copyright

© 2023 Daya Roopa & John. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)