Incorporating historical two-arm data in clinical trials with binary outcome: A practical approach.
clinical trials
historical data
power prior
sample size determination
type I error rate
Journal
Pharmaceutical statistics
ISSN: 1539-1612
Titre abrégé: Pharm Stat
Pays: England
ID NLM: 101201192
Informations de publication
Date de publication:
09 2020
09 2020
Historique:
received:
12
10
2018
revised:
03
03
2020
accepted:
18
03
2020
pubmed:
1
4
2020
medline:
2
7
2021
entrez:
1
4
2020
Statut:
ppublish
Résumé
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.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
662-678Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2020 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd.
Références
Viele K, Berry S, Neuenschwander B, et al. Use of historical control data for assessing treatment effects in clinical trials. Pharm Stat. 2014;13(1):41-54.
Chen MH, Ibrahim JG, Lam P, Yu A, Zhang Y. Bayesian design of noninferiority trials for medical devices using historical data. Biometrics. 2011;67(3):1163-1170.
Chen MH, Ibrahim JG, Shao QM. Power prior distributions for generalized linear models. J Stat Plan Infer. 2000;84(1-2):121-137.
Duan YY, Smith EP, Ye KY. Using power priors to improve the binomial test of water quality. J Agricul Biol Environ Stat. 2006;11(2):151-168.
Hobbs BP, Carlin BP, Mandrekar SJ, Sargent DJ. Hierarchical commensurate and power prior models for adaptive incorporation of historical information in clinical trials. Biometrics. 2011;67(3):1047-1056.
Rietbergen C, Klugkist I, Janssen KJ, Moons KG, Hoijtink HJ. Incorporation of historical data in the analysis of randomized therapeutic trials. Contemp Clin Trials. 2011;32(6):848-855.
Schmidli H, Gsteiger S, Roychoudhury S, O'Hagan A, Spiegelhalter D, Neuenschwander B. Robust meta-analytic-predictive priors in clinical trials with historical control information. Biometrics. 2014;70(4):1023-1032.
Gamalo-Siebers M, Savic J, Basu C, et al. Statistical modeling for Bayesian extrapolation of adult clinical trial information in pediatric drug evaluation. Pharm Stat. 2017;16(4):232-249.
Weber S, Gelman A, Lee D, Betancourt M, Vehtari A, Racine-Poon A. Bayesian aggregation of average data: an application in drug development. Ann Appl Stat. 2018;12(3):1583-1604.
Ibrahim JG, Chen MH, Gwon Y, Chen F. The power prior: theory and applications. Stat Med. 2015;34(28):3724-3749.
Gravestock I, Held L, C.O.-N. consortium. Adaptive power priors with empirical Bayes for clinical trials. Pharm Stat. 2017;16(5):349-360.
Seide SE, Röver C, Friede T. Likelihood-based random-effects meta-analysis with few studies: empirical and simulation studies. BMC Med Res Methodol. 2019;19(1):16.
Ibrahim JG, Chen MH. Power prior distributions for regression models. Stat Sci. 2000;15(1):46-60.
Kawasaki Y, Miyaoka E. A Bayesian inference of P(pi1 > pi2) for two proportions. J Biopharm Stat. 2012;22(3):425-437.
Lee PM. Bayesian Statistics: An Introduction. Arnold, London: John Wiley; 2012.
Howard J. The 2× 2 table: a discussion from a Bayesian viewpoint. Stat Sci. 1998;13(4):351-367.
Altham PM. Exact Bayesian analysis of a 2 times 2 contingency table, and Fisher' “exact” significance test. J R Stat Soc B Methodol. 1969;31(2):261-269.
Nurminen M, Mutanen P. Exact Bayesian analysis of two proportions. Scand J Stat. 1987;14(1):67-77.
Liebermeister K. Über Wahrscheinlichkeitsrechnung in Anwendung auf therapeutische Statistik. Sammlung Klinischer Vorträge (Innere Medicin No. 31-64)110, 935-962 Breitkopf & Härtel; 1877.
Lesaffre E, Baio G, Boulanger B. Bayesian Methods in Pharmaceutical Research. Boca Raton: CRC Press; 2020.
Zaslavsky BG. Bayesian hypothesis testing in two-arm trials with dichotomous outcomes. Biometrics. 2013;69(1):157-163.
Lydersen S, Fagerland MW, Laake P. Recommended tests for association in 2× 2 tables. Stat Med. 2009;28(7):1159-1175.
Fagerland MW, Lydersen S, Laake P. Statistical Analysis of Contingency Tables. Boca Raton: CRC Press/Taylor & Francis Group; 2017:634.
Berger RL, Boos DD. P values maximized over a confidence set for the nuisance parameter. J Am Stat Assoc. 1994;89(427):1012-1016.
Lydersen S, Langaas M, Bakke Ø. The exact unconditional z-pooled test for equality of two binomial probabilities: optimal choice of the Berger and Boos confidence coefficient. J Stat Comput Simul. 2012;82(9):1311-1316.
Yu G. Variance stabilizing transformations of Poisson, binomial and negative binomial distributions. Stat Probab Lett. 2009;79(14):1621-1629.
Khanna D, Denton CP, Jahreis A, et al. Safety and efficacy of subcutaneous tocilizumab in adults with systemic sclerosis (faSScinate): a phase 2, randomised, controlled trial. Lancet. 2016;387(10038):2630-2640.
Kopp-Schneider A, Calderazzo S, Wiesenfarth M. Power gains by using external information in clinical trials are typically not possible when requiring strict type I error control. Biom J. 2020;62(2):361-374.
Pocock SJ. The combination of randomized and historical controls in clinical trials. J Chronic Dis. 1976;29(3):175-188.