Volume 13, Issue 1 pp. 41-54
Main Paper

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

Kert Viele

Corresponding Author

Kert Viele

Berry Consultants, Austin, TX, USA

Correspondence to: Kert Viele, Berry Consultants, Austin, TX, USA.

E-mail: [email protected]

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Scott Berry

Scott Berry

Berry Consultants, Austin, TX, USA

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Beat Neuenschwander

Beat Neuenschwander

Novartis Pharma, CIS, Basel, Switzerland

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Billy Amzal

Billy Amzal

LA-SER Analytica, London, UK

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Fang Chen

Fang Chen

SAS, Cary, NC, USA

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Nathan Enas

Nathan Enas

Eli Lilly & Company, Indianapolis, IN, USA

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Brian Hobbs

Brian Hobbs

MD Anderson, Houston, TX, USA

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Joseph G. Ibrahim

Joseph G. Ibrahim

University of North Carolina, Chapel Hill, NC, USA

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Nelson Kinnersley

Nelson Kinnersley

F. Hoffman La Roche, Welwyn Garden City, Hertfordshire, UK

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Stacy Lindborg

Stacy Lindborg

Biogen IDEC, Cambridge, MA, USA

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Sandrine Micallef

Sandrine Micallef

Sanofi-Aventis R&D, Paris, France

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Satrajit Roychoudhury

Satrajit Roychoudhury

Novartis, East Hanover, NJ, USA

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Laura Thompson

Laura Thompson

US Food and Drug Administration, Rockville, MD, USA

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First published: 05 August 2013
Citations: 387

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.

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