TY - JOUR
T1 - Bayesian Methods in Regulatory Science
AU - Rosner, Gary L.
N1 - Funding Information:
Some of this work was supported by grant NCI P30CA006973 and by a Center of Excellence in Regulatory Science and Innovation (CERSI) grant to Johns Hopkins University from the U.S. Food & Drug Administration U01FD005942. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the HHS or FDA. The author gratefully acknowledges constructive comments from three reviewers and an associate editor that improved the article.
Publisher Copyright:
© 2019, © 2019 American Statistical Association.
PY - 2020/4/2
Y1 - 2020/4/2
N2 - 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 article 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.
AB - 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 article 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.
KW - Clinical trials
KW - Decision theory
KW - Study design
UR - http://www.scopus.com/inward/record.url?scp=85074948904&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074948904&partnerID=8YFLogxK
U2 - 10.1080/19466315.2019.1668843
DO - 10.1080/19466315.2019.1668843
M3 - Article
C2 - 32489520
AN - SCOPUS:85074948904
SN - 1946-6315
VL - 12
SP - 130
EP - 136
JO - Statistics in Biopharmaceutical Research
JF - Statistics in Biopharmaceutical Research
IS - 2
ER -