Critical appraisal of Bayesian dynamic borrowing from an imperfectly commensurate historical control

Bayesian dynamic borrowing designs facilitate borrowing information from historical studies. Historical data, when perfectly commensurate with current data, have been shown to reduce the trial duration and the sample size, while inflation in the type I error and reduction in the power have been reported, when imperfectly commensurate. These results, however, were obtained without considering that Bayesian designs are calibrated to meet regulatory requirements in practice and even no-borrowing designs may use information from historical data in the calibration. The implicit borrowing of historical data suggests that imperfectly commensurate historical data may similarly impact no-borrowing designs negatively. We will provide a fair appraiser of Bayesian dynamic borrowing and no-borrowing designs. We used a published selective adaptive randomization design and real clinical trial setting and conducted simulation studies under varying degrees of imperfectly commensurate historical control scenarios. The type I error was inflated under the null scenario of no intervention effect, while larger inflation was noted with borrowing. The larger inflation in type I error under the null setting can be offset by the greater probability to stop early correctly under the alternative. Response rates were estimated more precisely and the average sample size was smaller with borrowing. The expected increase in bias with borrowing was noted, but was negligible. Using Bayesian dynamic borrowing designs may improve trial efficiency by stopping trials early correctly and reducing trial length at the small cost of inflated type I error.

[1]  S J Pocock,et al.  The combination of randomized and historical controls in clinical trials. , 1976, Journal of chronic diseases.

[2]  Keying Ye,et al.  Evaluating water quality using power priors to incorporate historical information , 2006 .

[3]  Emmanuel Lesaffre,et al.  Use of a historical control group in a noninferiority trial assessing a new antibacterial treatment: A case study and discussion of practical implementation aspects , 2018, Pharmaceutical statistics.

[4]  Joseph G Ibrahim,et al.  Bayesian sequential meta‐analysis design in evaluating cardiovascular risk in a new antidiabetic drug development program , 2014, Statistics in medicine.

[5]  Leonhard Held,et al.  Adaptive power priors with empirical Bayes for clinical trials , 2017, Pharmaceutical statistics.

[6]  Bradley P Carlin,et al.  Hierarchical Commensurate and Power Prior Models for Adaptive Incorporation of Historical Information in Clinical Trials , 2011, Biometrics.

[7]  Nusrat Harun,et al.  Bayesian selective response‐adaptive design using the historical control , 2018, Statistics in medicine.

[8]  P. Thall,et al.  Practical Bayesian adaptive randomisation in clinical trials. , 2007, European journal of cancer.

[9]  Joseph P. Broderick,et al.  Tissue plasminogen activator for acute ischemic stroke. The National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group. , 1995 .

[10]  G. Yin,et al.  Worth Adapting? Revisiting the Usefulness of Outcome-Adaptive Randomization , 2012, Clinical Cancer Research.

[11]  D. Berry Bayesian clinical trials , 2006, Nature Reviews Drug Discovery.

[12]  Joseph G. Ibrahim,et al.  The relationship between the power prior and hierarchical models , 2006 .

[13]  N W Galwey,et al.  Supplementation of a clinical trial by historical control data: is the prospect of dynamic borrowing an illusion? , 2017, Statistics in medicine.

[14]  Michael Branson,et al.  A note on the power prior , 2009, Statistics in medicine.

[15]  Michael D Hill,et al.  Endovascular therapy after intravenous t-PA versus t-PA alone for stroke. , 2013, The New England journal of medicine.

[16]  Joseph P. Broderick,et al.  Methodology of the Interventional Management of Stroke III Trial , 2008, International journal of stroke : official journal of the International Stroke Society.

[17]  Jing Ning,et al.  Using short‐term response information to facilitate adaptive randomization for survival clinical trials , 2009, Statistics in medicine.

[18]  Mi-Ok Kim,et al.  Outcome‐adaptive randomization for a delayed outcome with a short‐term predictor: imputation‐based designs , 2014, Statistics in medicine.

[19]  J. Ibrahim,et al.  Power prior distributions for regression models , 2000 .

[20]  Brian P Hobbs,et al.  Detecting and accounting for violations of the constancy assumption in non-inferiority clinical trials , 2018, Statistical methods in medical research.

[21]  Brian P Hobbs,et al.  Adaptive adjustment of the randomization ratio using historical control data , 2013, Clinical trials.

[22]  G. Liu A dynamic power prior for borrowing historical data in noninferiority trials with binary endpoint , 2018, Pharmaceutical statistics.

[23]  Anthony O'Hagan,et al.  Robust meta‐analytic‐predictive priors in clinical trials with historical control information , 2014, Biometrics.

[24]  Heinz Schmidli,et al.  Incorporating historical information in biosimilar trials: Challenges and a hybrid Bayesian‐frequentist approach , 2018, Biometrical journal. Biometrische Zeitschrift.

[25]  T. J. Mitchell,et al.  Bayesian Variable Selection in Linear Regression , 1988 .

[26]  Feifang Hu,et al.  Doubly adaptive biased coin designs with delayed responses , 2008 .

[27]  Bradley P Carlin,et al.  Commensurate Priors for Incorporating Historical Information in Clinical Trials Using General and Generalized Linear Models. , 2012, Bayesian analysis.

[28]  R. Cuffe The inclusion of historical control data may reduce the power of a confirmatory study , 2011, Statistics in medicine.

[29]  Joseph G Ibrahim,et al.  Bayesian Meta‐Experimental Design: Evaluating Cardiovascular Risk in New Antidiabetic Therapies to Treat Type 2 Diabetes , 2012, Biometrics.