Incorporating historical two-arm data in clinical trials with binary outcome

• Verfasst von: Feißt, Manuel [VerfasserIn]
• Verfasst von: Krisam, Johannes [VerfasserIn]
• Verfasst von: Kieser, Meinhard [VerfasserIn]
• Titel: Incorporating historical two-arm data in clinical trials with binary outcome
• Titelzusatz: A practical approach
• Verf.angabe: Manuel Feißt, Johannes Krisam, Meinhard Kieser
• Jahr: 2020
• Umfang: 17 S.
• Fussnoten: First published: 30 March 2020 ; Gesehen am 10.01.2021
• Titel Quelle: Enthalten in: Pharmaceutical statistics
• Ort Quelle: New York, NY : Wiley, 2002
• Jahr Quelle: 2020
• Band/Heft Quelle: 19(2020), 5, Seite 662-678
• ISSN Quelle: 1539-1612
• DOI: doi:10.1002/pst.2023
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: clinical trials
• Sach-SW: historical data
• Sach-SW: power prior
• Sach-SW: sample size determination
• Sach-SW: type I error rate
• K10plus-PPN: 1785372440
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

The feasibility of a new clinical trial may be increased by incorporating historical data of previous trials. In the particular case where only data from a single historical trial are available, there exists no clear recommendation in the literature regarding the most favorable approach. A main problem of the incorporation of historical data is the possible inflation of the type I error rate. A way to control this type of error is the so-called power prior approach. This Bayesian method does not “borrow” the full historical information but uses a parameter 0 ≤ δ ≤ 1 to determine the amount of borrowed data. Based on the methodology of the power prior, we propose a frequentist framework that allows incorporation of historical data from both arms of two-armed trials with binary outcome, while simultaneously controlling the type I error rate. It is shown that for any specific trial scenario a value δ > 0 can be determined such that the type I error rate falls below the prespecified significance level. The magnitude of this value of δ depends on the characteristics of the data observed in the historical trial. Conditionally on these characteristics, an increase in power as compared to a trial without borrowing may result. Similarly, we propose methods how the required sample size can be reduced. The results are discussed and compared to those obtained in a Bayesian framework. Application is illustrated by a clinical trial example.