Received: 6 July 2016 Revised: 20 September 2017 Accepted: 6 November 2017 DOI: 10.1002/pst.1843 MAIN PAPER Use of a historical control group in a noninferiority trial assessing a new antibacterial treatment: A case study and discussion of practical implementation aspects David Dejardin 1 Paul Delmar 1 Charles Warne 2 Katie Patel 2 Joost van Rosmalen 3 Emmanuel Lesaffre 4 1 Department of Biostatistics, F. Hoffmann-La Roche, Basel, Switzerland 2 Department of Biostatistics, Roche Products Ltd, Welwyn Garden City, UK 3 Department of Biostatistics, Erasmus University Medical Center, Rotterdam, the Netherlands 4 Interuniversity Institute for Biostatistics and Statistical Bioinformatics, KU Leuven, Leuven, Belgium Correspondence David Dejardin, Department of Biostatistics, F. Hoffmann-La Roche, Basel, Switzerland. Email: david.dejardin@roche.com When recruitment into a clinical trial is limited due to rarity of the disease of interest, or when recruitment to the control arm is limited due to ethical reasons (eg, pediatric studies or important unmet medical need), exploiting historical controls to augment the prospectively collected database can be an attractive option. Statistical methods for combining historical data with randomized data, while accounting for the incompatibility between the two, have been recently proposed and remain an active field of research. The current literature is lacking a rigorous comparison between methods but also guidelines about their use in practice. In this paper, we compare the existing methods based on a confirma- tory phase III study design exercise done for a new antibacterial therapy with a binary endpoint and a single historical dataset. A procedure to assess the relative performance of the different methods for borrowing information from histori- cal control data is proposed, and practical questions related to the selection and implementation of methods are discussed. Based on our examination, we found that the methods have a comparable performance, but we recommend the robust mixture prior for its ease of implementation. KEYWORDS commensurate prior, historical control, Bayesian analysis, power prior, robust mixture prior 1 INTRODUCTION Statistical methods for combining historical controls with randomized clinical trial data constitutes an active field of research. Methods have been recently introduced to account for incompatibility between the historical control data and the randomized control data. As discussed in the seminal article by Pocock, 1 even if historical and randomized control data are strictly matched, a difference in central tendency or “drift” between historical and randomized control can never be totally ruled out and should be accounted for in the analysis. We will use here the term “compatibility” for “absence of drift.” In principle, the risk of bias due to the drift between randomized and historical controls could be mitigated by the use of a “dynamic borrowing” approach, where the degree of borrowing depends on the compatibility of the randomized and historical control data. This paper takes the perspective of a clinical trial practitioner in comparing these methods. P. Delmar and D. Dejardin have contributed equally to the manuscript. Pharmaceutical Statistics. 2017;1–13. wileyonlinelibrary.com/journal/pst Copyright © 2017 John Wiley & Sons, Ltd. 1