Bayesian leveraging of historical control data for a clinical trial with time-to-event endpoint.
Historical data
hierarchical model
meta-analysis
piecewise exponential model
prior distribution
time-to-event data
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
30 03 2020
30 03 2020
Historique:
received:
25
07
2019
revised:
22
11
2019
accepted:
01
12
2019
pubmed:
28
1
2020
medline:
22
6
2021
entrez:
28
1
2020
Statut:
ppublish
Résumé
The recent 21st Century Cures Act propagates innovations to accelerate the discovery, development, and delivery of 21st century cures. It includes the broader application of Bayesian statistics and the use of evidence from clinical expertise. An example of the latter is the use of trial-external (or historical) data, which promises more efficient or ethical trial designs. We propose a Bayesian meta-analytic approach to leverage historical data for time-to-event endpoints, which are common in oncology and cardiovascular diseases. The approach is based on a robust hierarchical model for piecewise exponential data. It allows for various degrees of between trial-heterogeneity and for leveraging individual as well as aggregate data. An ovarian carcinoma trial and a non-small cell cancer trial illustrate methodological and practical aspects of leveraging historical data for the analysis and design of time-to-event trials.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
984-995Informations de copyright
© 2020 John Wiley & Sons, Ltd.
Références
European Medicines Agency. Innovative medicines initiative 2: Europe's fast track to better medicines; 2014.
US Food and Drug Administration. PDUFA Reauthorization Performance Goals And Procedures Fiscal Years 2018 Through 2022. 2018.
US Congress. 21st Century Cures Act (Public Law 114-255, 130 STAT 1033-1344); 2016.
French Jacqueline A, Temkin Nancy R, Shneker Bassel F, et al. Conversion to monotherapy: first study using a historical control group. Neurotherapeutics. 2012;9(1):176-184.
Hueber Wolfgang SBE, Steve L, et al. Secukinumab, a human anti-IL-17A monoclonal antibody, for moderate to severe Crohn's disease: unexpected results of a randomised, double-blind placebo-controlled trial. Gut. 2012;61(12):1693-1700.
Campbell G. Bayesian methods in clinical trials with applications to medical devices. Commun Stat Appl Methods. 2017;24(6):561-581.
Egger M, Smith GD, Altman DG. Systematic Reviews in Health Care: Meta-Analysis in Context. London: BMJ Publishing Group; 1995.
European Medicines Agency: Committee for Medicinal Products for Human Use (CHMP). Points to Consider in Application with 1. Meta-analysis; 2. One Pivotal Study; CPMP/EWP/2330/99; 2001.
Rietbergen C, Klugkist I, Janssen KJM, Moons KGM, Hoijtink HJA. Incorporation of historical data in the analysis of randomized therapeutic trials. Contemp Clin Trials. 2011;32(6):848-855.
Pocock SJ. The combination of randomized and historical controls in clinical trials. J Chronic Dis. 1976;29:175-188.
Ibrahim JG, Chen M-H. Power prior distributions for regression models. Stat Sci. 2000;15(1):46-60.
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.
Spiegelhalter DJ, Abrams KR, Myles JP. Bayesian Approaches to Clinical Trials and Health-care Evaluation. New York, NY: John Wiley & Sons; 2004.
Neuenschwander B, Capkun-Niggli G, Branson M, Spiegelhalter DJ. Summarizing historical information on controls in clinical trials. Clin Trials. 2010;7(1):5-18.
Heinz S, Sandro G, Satrajit R, Anthony O'H, David S, Beat N. Robust meta-analytic-predictive priors in clinical trials with historical control information. Biometrics. 2014;70(4):1023-1032.
Kert V, Scott B, Beat N, et al. Use of historical control data for assessing treatment effects in clinical trials. Pharm Stat. 2014;13(1):41-54.
Lewis CJ, Sarkar S, Zhu J, Carlin BP. Borrowing from historical control data in cancer drug development: a cautionary tale and practical guidelines. Stat Biopharm Res. 2019;11(1):67-78.
Murray TA, Hobbs BP, Lystig TC, Carlin BP. Semiparametric Bayesian commensurate survival model for post-market medical device surveillance with non-exchangeable historical data. Biometrics. 2014;70(1):185-191.
Bertsche A, Fleischer F, Beyersmann J, Nehmiz G. Bayesian phase II optimization for time-to-event data based on historical information. Stat Methods Med Res. 2017;4:1-18.
Hobbs BP, Carlin BP, Sargent DJ. Adaptive adjustment of the randomization ratio using historical control data. Clin Trials. 2013;10(3):430-440.
Malec D. A closer look at combining data among a small number of binomial experiments. Stat Med. 2001;20(12):1811-1824.
Morita S, Thall PF, Müller P. Determining the effective sample size of a parametric prior. Biometrics. 2008;64(2):595-602.
Pennello G, Thompson L. Experience with reviewing Bayesian medical device trials. J Biopharm Stat. 2007;18(1):81-115.
Neuenschwander B, Weber S, Schmidli H, O'Hagan A. Predictively consistent prior effective sample sizes. Biometrics. 2019; (forthcoming).
Dalal SR, Hall WJ. Approximating priors by mixtures of natural conjugate priors. J R Stat Soc B. 1983;45(2):278-286.
Diaconis P, Ylvisaker D. Quantifying prior opinion. Bayesian Statistics 2. In: Bernardo J, DeGroot M, Lindley D, Smith A, eds. Proceedings of the Second Valencia International Meeting September 6-10 1983. North-Holland, Amsterdam: Elsevier; 1985:133-156.
SAS Institute. SAS User Guide: Statistics. The FMM Procedure. Cary, NC: SAS Institute Inc; 2014.
Weber Sebastian. RBesT: R Bayesian evidence synthesis tools. R package version 1.4-0; 2019.
Sebastian W, Yue L, John S, Tomoyuki K, Heinz S. Applying meta-Analytic predictive priors with the R Bayesian evidence synthesis tools. arXiv. 2019; e-prints.
Voest EE, Houwelingen Van JC, Neijt JP. A meta-analysis of prognostic factors in advanced ovarian cancer with median survival and overall survival (measured with the log (relative risk)) as main objectives. Eur J Cancer Clin Oncol. 1989;25:711-720.
Fiocco M, Putter H, Houwelingen Van JC. Meta-analysis of pairs of survival curves under heterogeneity: a Poisson correlated gamma-frailty approach. Stat Med. 2009;28(30):3782-3797.
Parmar MKB, Torri V, Stewart L. Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints. Stat Med. 1998;17(24):2815-2834.
WHO cancer factsheet. World Health Organization; 2018.
Neuenschwander B, Roychoudhury S, Schmidli H. On the use of co-data in clinical trials. Stat Biopharm Res. 2016;8(3):345-354.
European Medicines Agency. Committee for medicinal products for human use (CHMP). Guideline on Clinical Trials in Small Populations; 2006.
European Medicines Agency: Committee for Proprietary Medicinal Products (CPMP). Note for Guidance on Clinical Investigation of Medicinal Products in the Pediatric Population. 2001.
European Medicines Agency: Committee for Medicinal Products for Human Use (CHMP). Guideline on the Choice of the Non-Inferiority Margin. 2006.
US Food and Drug Administration. Draft Guidance for Industry: Interacting with the FDA on Complex Innovative Trial Designs for Drugs and Biological Products. 2019.
Berry DA. Bayesian statistics and the efficiency and ethics of clinical trials. Stat Sci. 2004;19(1):175-187.
Turner RM, Davey J, Clarke MJ, Thompson SG, Higgin PT. Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews. Int J Epidemiol. 2012;41:818-827.
Turner RM, Jackson D, Wei Y, Thompson SG, Higgins PT. Predictive distributions for between-study heterogeneity and simple methods for their application in Bayesian meta-analysis. Stat Med. 2015;34:984-998.
Jessica L, Rosalind W, Jiacheng Y, et al. Minimizing patient burden through the use of historical subject-level data in innovative confirmatory clinical trials: Review of methods and opportunities. Ther Innov Regul Sci. 2018;52(5):546-559.
Mark Mitchell, Baurzhan Muftakhidinov, Winchen Tobias, et al. Engauge Digitizer Software. 2019.
Sturtz S, Ligges U, Gelman A. R2WinBUGS: a package for running WinBUGS from R. J Stat Softw. 2005;12(3):1-16.
Martyn P, Nicky B, Kate C, Karen V. CODA: convergence diagnosis and output analysis for MCMC. R News. 2006;6(1):7-11.
Therneau Terry M. A Package for Survival Analysis in S; 2015.
Venables WN, Ripley BD. Modern Applied Statistics with S. 4th ed. New York, NY: Springer; 2002.