Literature DB >> 23913901

Use of historical control data for assessing treatment effects in clinical trials.

Kert Viele1, Scott Berry, Beat Neuenschwander, Billy Amzal, Fang Chen, Nathan Enas, Brian Hobbs, Joseph G Ibrahim, Nelson Kinnersley, Stacy Lindborg, Sandrine Micallef, Satrajit Roychoudhury, Laura Thompson.   

Abstract

Clinical trials rarely, if ever, occur in a vacuum. Generally, large amounts of clinical data are available prior to the start of a study, particularly on the current study's control arm. There is obvious appeal in using (i.e., 'borrowing') this information. With historical data providing information on the control arm, more trial resources can be devoted to the novel treatment while retaining accurate estimates of the current control arm parameters. This can result in more accurate point estimates, increased power, and reduced type I error in clinical trials, provided the historical information is sufficiently similar to the current control data. If this assumption of similarity is not satisfied, however, one can acquire increased mean square error of point estimates due to bias and either reduced power or increased type I error depending on the direction of the bias. In this manuscript, we review several methods for historical borrowing, illustrating how key parameters in each method affect borrowing behavior, and then, we compare these methods on the basis of mean square error, power and type I error. We emphasize two main themes. First, we discuss the idea of 'dynamic' (versus 'static') borrowing. Second, we emphasize the decision process involved in determining whether or not to include historical borrowing in terms of the perceived likelihood that the current control arm is sufficiently similar to the historical data. Our goal is to provide a clear review of the key issues involved in historical borrowing and provide a comparison of several methods useful for practitioners.
Copyright © 2013 John Wiley & Sons, Ltd.

Entities:  

Keywords:  Bayesian; borrowing; historical data; priors

Mesh:

Year:  2013        PMID: 23913901      PMCID: PMC3951812          DOI: 10.1002/pst.1589

Source DB:  PubMed          Journal:  Pharm Stat        ISSN: 1539-1604            Impact factor:   1.894


  11 in total

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4.  Experience with reviewing Bayesian medical device trials.

Authors:  Gene Pennello; Laura Thompson
Journal:  J Biopharm Stat       Date:  2008       Impact factor: 1.051

5.  A note on the power prior.

Authors:  Beat Neuenschwander; Michael Branson; David J Spiegelhalter
Journal:  Stat Med       Date:  2009-12-10       Impact factor: 2.373

6.  Summarizing historical information on controls in clinical trials.

Authors:  Beat Neuenschwander; Gorana Capkun-Niggli; Michael Branson; David J Spiegelhalter
Journal:  Clin Trials       Date:  2010-02       Impact factor: 2.486

Review 7.  The combination of randomized and historical controls in clinical trials.

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8.  Bayesian design of noninferiority trials for medical devices using historical data.

Authors:  Ming-Hui Chen; Joseph G Ibrahim; Peter Lam; Alan Yu; Yuanye Zhang
Journal:  Biometrics       Date:  2011-03-01       Impact factor: 2.571

9.  Hierarchical commensurate and power prior models for adaptive incorporation of historical information in clinical trials.

Authors:  Brian P Hobbs; Bradley P Carlin; Sumithra J Mandrekar; Daniel J Sargent
Journal:  Biometrics       Date:  2011-03-01       Impact factor: 2.571

10.  Commensurate Priors for Incorporating Historical Information in Clinical Trials Using General and Generalized Linear Models.

Authors:  Brian P Hobbs; Daniel J Sargent; Bradley P Carlin
Journal:  Bayesian Anal       Date:  2012-08-28       Impact factor: 3.728

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  76 in total

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2.  Design and Evaluation of an External Control Arm Using Prior Clinical Trials and Real-World Data.

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5.  The Use and Misuse of Historical Controls in Regulatory Toxicology: Lessons from the CLARITY-BPA Study.

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Journal:  Endocrinology       Date:  2020-05-01       Impact factor: 4.736

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Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2019-08-09       Impact factor: 3.802

7.  Bayesian hierarchical models for network meta-analysis incorporating nonignorable missingness.

Authors:  Jing Zhang; Haitao Chu; Hwanhee Hong; Beth A Virnig; Bradley P Carlin
Journal:  Stat Methods Med Res       Date:  2015-07-28       Impact factor: 3.021

Review 8.  Trial Design Challenges and Approaches for Precision Oncology in Rare Tumors: Experiences of the Children's Oncology Group.

Authors:  Lindsay A Renfro; Lingyun Ji; Jin Piao; Arzu Onar-Thomas; John A Kairalla; Todd A Alonzo
Journal:  JCO Precis Oncol       Date:  2019-10-24

9.  The design of a Bayesian platform trial to prevent and eradicate inhibitors in patients with hemophilia.

Authors:  Marnie Bertolet; Maria M Brooks; Margaret V Ragni
Journal:  Blood Adv       Date:  2020-11-10

10.  Sensitivity to Excluding Treatments in Network Meta-analysis.

Authors:  Lifeng Lin; Haitao Chu; James S Hodges
Journal:  Epidemiology       Date:  2016-07       Impact factor: 4.822

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