Bayesian Methods in Regulatory Science

Overview

Journal Stat Biopharm Res

Date 2020 Jun 4

PMID 32489520

Citations 4

Authors

Gary L Rosner

Affiliations

Soon will be listed here.

Abstract

Regulatory science comprises the tools, standards, and approaches that regulators use to assess safety, efficacy, quality, and performance of drugs and medical devices. A major focus of regulatory science is the design and analysis of clinical trials. Clinical trials are an essential part of clinical research programs that aim to improve therapies and reduce the burden of disease. These clinical experiments help us learn about what works clinically and what does not work. The results of clinical trials support therapeutic and policy decisions. When designing clinical trials, investigators make many decisions regarding various aspects of how they will carry out the study, such as the primary objective of the study, primary and secondary endpoints, methods of analysis, sample size, etc. This paper provides a brief review of the clinical development of new treatments and argues for the use of Bayesian methods and decision theory in clinical research.

Citing Articles

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References

Schoenfeld D, Zheng H, Finkelstein D . Bayesian design using adult data to augment pediatric trials. Clin Trials. 2009; 6(4):297-304. PMC: 3374646. DOI: 10.1177/1740774509339238. View

Weinstein M, Torrance G, McGuire A . QALYs: the basics. Value Health. 2009; 12 Suppl 1:S5-9. DOI: 10.1111/j.1524-4733.2009.00515.x. View

Ramaekers B, Joore M, Grutters J, van den Ende P, de Jong J, Houben R . The impact of late treatment-toxicity on generic health-related quality of life in head and neck cancer patients after radiotherapy. Oral Oncol. 2011; 47(8):768-74. DOI: 10.1016/j.oraloncology.2011.05.012. View

Goodman S . Toward evidence-based medical statistics. 2: The Bayes factor. Ann Intern Med. 1999; 130(12):1005-13. DOI: 10.7326/0003-4819-130-12-199906150-00019. View

Carlin B, Kadane J, Gelfand A . Approaches for optimal sequential decision analysis in clinical trials. Biometrics. 1998; 54(3):964-75. View

Rossell D, Muller P, Rosner G . Screening designs for drug development. Biostatistics. 2006; 8(3):595-608. DOI: 10.1093/biostatistics/kxl031. View

Stallard N, Miller F, Day S, Hee S, Madan J, Zohar S . Determination of the optimal sample size for a clinical trial accounting for the population size. Biom J. 2016; 59(4):609-625. PMC: 5516263. DOI: 10.1002/bimj.201500228. View

Gamalo-Siebers M, Savic J, Basu C, Zhao X, Gopalakrishnan M, Gao A . Statistical modeling for Bayesian extrapolation of adult clinical trial information in pediatric drug evaluation. Pharm Stat. 2017; 16(4):232-249. DOI: 10.1002/pst.1807. View

Ding M, Rosner G, Muller P . Bayesian optimal design for phase II screening trials. Biometrics. 2007; 64(3):886-894. PMC: 3075947. DOI: 10.1111/j.1541-0420.2007.00951.x. View

10.

Meregaglia M, Cairns J . A systematic literature review of health state utility values in head and neck cancer. Health Qual Life Outcomes. 2017; 15(1):174. PMC: 5581467. DOI: 10.1186/s12955-017-0748-z. View

11.

Muller P, Xu Y, Thall P . Clinical Trial Design as a Decision Problem. Appl Stoch Models Bus Ind. 2017; 33(3):296-301. PMC: 5705102. DOI: 10.1002/asmb.2222. View

12.

Lewis R, Lipsky A, Berry D . Bayesian decision-theoretic group sequential clinical trial design based on a quadratic loss function: a frequentist evaluation. Clin Trials. 2007; 4(1):5-14. DOI: 10.1177/1740774506075764. View

13.

Ezzalfani M, Zohar S, Qin R, Mandrekar S, Le Deley M . Dose-finding designs using a novel quasi-continuous endpoint for multiple toxicities. Stat Med. 2013; 32(16):2728-46. PMC: 3813987. DOI: 10.1002/sim.5737. View

14.

Stallard N, Thall P, Whitehead J . Decision theoretic designs for phase II clinical trials with multiple outcomes. Biometrics. 2001; 55(3):971-7. DOI: 10.1111/j.0006-341x.1999.00971.x. View

15.

Sheiner L . Learning versus confirming in clinical drug development. Clin Pharmacol Ther. 1997; 61(3):275-91. DOI: 10.1016/S0009-9236(97)90160-0. View

16.

Campbell G . Bayesian statistics in medical devices: innovation sparked by the FDA. J Biopharm Stat. 2011; 21(5):871-87. DOI: 10.1080/10543406.2011.589638. View

17.

Johnson V, Cook J . Bayesian design of single-arm phase II clinical trials with continuous monitoring. Clin Trials. 2009; 6(3):217-26. DOI: 10.1177/1740774509105221. View

18.

Operskalski J, Barbey A . MEDICINE. Risk literacy in medical decision-making. Science. 2016; 352(6284):413-4. DOI: 10.1126/science.aaf7966. View

19.

Harrington D, DAgostino Sr R, Gatsonis C, Hogan J, Hunter D, Normand S . New Guidelines for Statistical Reporting in the . N Engl J Med. 2019; 381(3):285-286. DOI: 10.1056/NEJMe1906559. View

20.

Thall P, Cook J . Dose-finding based on efficacy-toxicity trade-offs. Biometrics. 2004; 60(3):684-93. DOI: 10.1111/j.0006-341X.2004.00218.x. View