TY - JOUR
T1 - Bayesian and Frequentist Approaches to Rescuing Disrupted Trials
T2 - A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions
AU - Kunz, Cornelia Ursula
AU - Tarima, Sergey
AU - Rosner, Gary L.
AU - Emsley, Richard
AU - Bauer, Madeline
AU - Jennison, Christopher
AU - Rosenberger, James L.
AU - Stallard, Nigel
AU - Zohar, Sarah
AU - Flournoy, Nancy
PY - 2024/3/19
Y1 - 2024/3/19
N2 - The COVID-19 pandemic impacted clinical trials in ways never expected. However, similar challenges should now be expected going forward. These challenges made us aware of statistical problems arising from other types of disruptions that had not previously captured the attention of the statistical community. This article describes some frequentist and Bayesian statistical tools that can be used with future disruptions and illuminates issues that could benefit from more statistical research. Disruptions may threaten a clinical trial’s validity. Here, we address two resultant challenges: (a) performing an unplanned analysis with options to stop and/or change the sample size; and (b) changes in the study population that are observable or unobservable at the patient level. Different paradigms lead to different ways of doing things, but many statisticians work exclusively within a Bayesian or frequentist paradigm. We propose and provide side-by-side descriptions of Bayesian and frequentist approaches to dealing with these challenges. An illustrative phase III trial aims to compare second-line therapies for type 2 diabetes. We compare and contrast Bayesian and frequentist coping strategies assuming the trial was interrupted due to COVID-19, focusing on Type I error control and the expected loss from a specific utility function.
AB - The COVID-19 pandemic impacted clinical trials in ways never expected. However, similar challenges should now be expected going forward. These challenges made us aware of statistical problems arising from other types of disruptions that had not previously captured the attention of the statistical community. This article describes some frequentist and Bayesian statistical tools that can be used with future disruptions and illuminates issues that could benefit from more statistical research. Disruptions may threaten a clinical trial’s validity. Here, we address two resultant challenges: (a) performing an unplanned analysis with options to stop and/or change the sample size; and (b) changes in the study population that are observable or unobservable at the patient level. Different paradigms lead to different ways of doing things, but many statisticians work exclusively within a Bayesian or frequentist paradigm. We propose and provide side-by-side descriptions of Bayesian and frequentist approaches to dealing with these challenges. An illustrative phase III trial aims to compare second-line therapies for type 2 diabetes. We compare and contrast Bayesian and frequentist coping strategies assuming the trial was interrupted due to COVID-19, focusing on Type I error control and the expected loss from a specific utility function.
KW - Decision-theoretic designs
KW - Flexible designs
KW - Informative interim adaptations
KW - Population changes
KW - Sample size re-estimation
UR - http://www.scopus.com/inward/record.url?scp=85188822145&partnerID=8YFLogxK
U2 - 10.1080/19466315.2024.2313986
DO - 10.1080/19466315.2024.2313986
M3 - Article
AN - SCOPUS:85188822145
SN - 1946-6315
JO - Statistics in Biopharmaceutical Research
JF - Statistics in Biopharmaceutical Research
ER -