Bayesian and Frequentist Approaches to Rescuing Disrupted Trials: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

Cornelia Ursula Kunz, Sergey Tarima, Gary L. Rosner, Richard Emsley, Madeline Bauer, Christopher Jennison, James L. Rosenberger, Nigel Stallard, Sarah Zohar, Nancy Flournoy

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Number of pages20
JournalStatistics in Biopharmaceutical Research
Early online date19 Mar 2024
DOIs
Publication statusE-pub ahead of print - 19 Mar 2024

Keywords

  • Decision-theoretic designs
  • Flexible designs
  • Informative interim adaptations
  • Population changes
  • Sample size re-estimation

ASJC Scopus subject areas

  • Statistics and Probability
  • Pharmaceutical Science

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