Calibrated dynamic borrowing using capping priors

Sharon X. Ling, Brian P. Hobbs, Alexander M. Kaizer, Joseph S. Koopmeiners

Research output: Contribution to journalArticlepeer-review

Abstract

Multisource exchangeability models (MEMs), a BayeTsian approach for dynamically integrating information from multiple clinical trials, are a promising approach for gaining efficiency in randomized controlled trials. When the supplementary trials are considerably larger than the primary trial, care must be taken when integrating supplementary data to avoid overwhelming the primary trial. In this paper, we propose “capping priors,” which controls the extent of dynamic borrowing by placing an a priori cap on the effective supplemental sample size. We demonstrate the behavior of this technique via simulation, and apply our method to four randomized trials of very low nicotine content cigarettes.

Original languageEnglish (US)
Pages (from-to)852-867
Number of pages16
JournalJournal of Biopharmaceutical Statistics
Volume31
Issue number6
DOIs
StatePublished - 2021

Bibliographical note

Funding Information:
This work was supported in part by the NSF Graduate Research Fellowship Program (to S. Ling) and by NIH grants R01-DA046320 and U54-DA031659 from the National Institute on Drug Abuse and FDA Center for Tobacco Products (CTP), and K01-HL151754 from the National Heart, Lung, and Blood Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or Food and Drug Administration Center for Tobacco Products. We would like to thank our collaborators, Drs. Eric Donny, Dorothy Hatsukami, and Jen Tidey, for providing access to the data used for illustration in Section 5.

Publisher Copyright:
© 2022 Taylor & Francis Group, LLC.

Keywords

  • capping priors
  • Multisource exchangeability models
  • prior specification
  • reduced nicotine content cigarettes
  • supplementary data
  • Sample Size
  • Computer Simulation
  • Humans
  • Bayes Theorem
  • Research Design

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

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