Treffer: Prospectively Specified Adaptive Bayesian Borrowing: Considerations, Methodologies, and Implementations.
Title:
Prospectively Specified Adaptive Bayesian Borrowing: Considerations, Methodologies, and Implementations.
Authors:
Source:
Pharmaceutical statistics [Pharm Stat] 2026 Jan-Feb; Vol. 25 (1), pp. e70051.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wiley Country of Publication: England NLM ID: 101201192 Publication Model: Print Cited Medium: Internet ISSN: 1539-1612 (Electronic) Linking ISSN: 15391604 NLM ISO Abbreviation: Pharm Stat Subsets: MEDLINE
Imprint Name(s):
Original Publication: Chichester, UK : Wiley, c2002-
MeSH Terms:
References:
M. J. Field, R. E. Behrman, and Institute of Medicine (US) Committee on Clinical Research Involving Children, “The Necessity and Challenges of Clinical Research Involving Children,” in Ethical Conduct of Clinical Research Involving Children, ed. M. J. Field and R. E. Behrman (National Academies Press, 2004), 58–92.
E. Spanakis, M. Kron, M. Bereswill, and S. Mukhopadhyay, “Addressing Statistical Issues When Leveraging External Control Data in Pediatric Clinical Trials Using Bayesian Dynamic Borrowing,” Journal of Biopharmaceutical Statistics 33, no. 6 (2023): 752–769.
B. Neuenschwander, G. Capkun‐Niggli, M. Branson, and D. J. Spiegelhalter, “Summarizing Historical Information on Controls in Clinical Trials,” Clinical Trials 7, no. 1 (2010): 5–18.
H. Schmidli, S. Gsteiger, S. Roychoudhury, A. O'Hagan, D. Spiegelhalter, and B. Neuenschwander, “Robust Meta‐Analytic‐Predictive Priors in Clinical Trials With Historical Control Information,” Biometrics 70, no. 4 (2014): 1023–1032.
B. Neuenschwander, S. Wandel, S. Roychoudhury, and S. Bailey, “Robust Exchangeability Designs for Early Phase Clinical Trials With Multiple Strata,” Pharmaceutical Statistics 15, no. 2 (2016): 123–134.
E. G. Ryan, J. Bruce, A. J. Metcalfe, et al., “Using Bayesian Adaptive Designs to Improve Phase III Trials: A Respiratory Care Example,” BMC Medical Research Methodology 19, no. 1 (2019): 99.
M. G. Aman and N. N. Singh, Aberrant Behavior Checklist (Slosson, 1986).
H. Ichikawa, K. Mikami, T. Okada, et al., “Aripiprazole in the Treatment of Irritability in Children and Adolescents With Autism Spectrum Disorder in Japan: A Randomized, Double‐Blind, Placebo‐Controlled Study,” Child Psychiatry and Human Development 48, no. 5 (2017): 796–806, https://doi.org/10.1007/s10578‐016‐0704‐x.
R. N. Marcus, R. Owen, L. Kamen, et al., “A Placebo‐Controlled, Fixed‐Dose Study of Aripiprazole in Children and Adolescents With Irritability Associated With Autistic Disorder,” Journal of the American Academy of Child and Adolescent Psychiatry 48 (2009): 1110–1119.
R. Owen, L. Sikich, R. N. Marcus, et al., “Aripiprazole in the Treatment of Irritability in Children and Adolescents With Autistic Disorder,” Pediatrics 124, no. 6 (2009): 1533–1540, https://doi.org/10.1542/peds.2008‐3782.
J. T. McCracken, J. McGough, B. Shah, et al., “Risperidone in Children With Autism and Serious Behavioral Problems,” New England Journal of Medicine 347, no. 5 (2002): 314–321, https://doi.org/10.1056/NEJMoa013171.
S. A. Montgomery and M. A. Åsberg, “A New Depression Scale Designed to Be Sensitive to Change,” British Journal of Psychiatry 134, no. 4 (1979): 382–389.
G. S. Sachs, P. P. Yeung, L. Rekeda, A. Khan, J. L. Adams, and M. Fava, “Adjunctive Cariprazine for the Treatment of Patients With Major Depressive Disorder: A Randomized, Double‐Blind, Placebo‐Controlled Phase 3 Study,” American Journal of Psychiatry 180, no. 3 (2023): 241–251.
R. Riesenberg, P. P. Yeung, L. Rekeda, G. S. Sachs, M. Kerolous, and M. Fava, “Cariprazine for the Adjunctive Treatment of Major Depressive Disorder in Patients With Inadequate Response to Antidepressant Therapy: Results of a Randomized, Double‐Blind, Placebo‐Controlled Study,” Journal of Clinical Psychiatry 84, no. 5 (2023): 48439.
S. Durgam, W. Earley, H. Guo, et al., “Efficacy and Safety of Adjunctive Cariprazine in Inadequate Responders to Antidepressants: A Randomized, Double‐Blind, Placebo‐Controlled Study in Adult Patients With Major Depressive Disorder,” Journal of Clinical Psychiatry 77, no. 3 (2016): 6112.
R. E. Kass and A. E. Raftery, “Bayes Factors,” Journal of the American Statistical Association 90, no. 430 (1995): 773–795.
J. G. Ibrahim and M.‐H. Chen, “Power Prior Distributions for Regression Models,” Statistical Science 15, no. 1 (2000): 46–60.
B. P. Hobbs, D. J. Sargent, and B. P. Carlin, “Commensurate Priors for Incorporating Historical Information in Clinical Trials Using General and Generalized Linear Models,” Bayesian Analysis 7, no. 3 (2012): 639–674.
B. Neuenschwander, S. Weber, H. Schmidli, and A. O'Hagan, “Predictively Consistent Prior Effective Sample Sizes,” Biometrics 76, no. 2 (2020): 578–587.
T. Friede and M. Kieser, “Sample Size Recalculation in Internal Pilot Study Designs: A Review,” Biometrical Journal 48, no. 4 (2006): 537–555.
E. Spanakis, M. Kron, M. Bereswill, and S. Mukhopadhyay, “Addressing Statistical Issues When Leveraging External Control Data in Pediatric Clinical Trials Using Bayesian Dynamic Borrowing,” Journal of Biopharmaceutical Statistics 33, no. 6 (2023): 752–769.
B. Neuenschwander, G. Capkun‐Niggli, M. Branson, and D. J. Spiegelhalter, “Summarizing Historical Information on Controls in Clinical Trials,” Clinical Trials 7, no. 1 (2010): 5–18.
H. Schmidli, S. Gsteiger, S. Roychoudhury, A. O'Hagan, D. Spiegelhalter, and B. Neuenschwander, “Robust Meta‐Analytic‐Predictive Priors in Clinical Trials With Historical Control Information,” Biometrics 70, no. 4 (2014): 1023–1032.
B. Neuenschwander, S. Wandel, S. Roychoudhury, and S. Bailey, “Robust Exchangeability Designs for Early Phase Clinical Trials With Multiple Strata,” Pharmaceutical Statistics 15, no. 2 (2016): 123–134.
E. G. Ryan, J. Bruce, A. J. Metcalfe, et al., “Using Bayesian Adaptive Designs to Improve Phase III Trials: A Respiratory Care Example,” BMC Medical Research Methodology 19, no. 1 (2019): 99.
M. G. Aman and N. N. Singh, Aberrant Behavior Checklist (Slosson, 1986).
H. Ichikawa, K. Mikami, T. Okada, et al., “Aripiprazole in the Treatment of Irritability in Children and Adolescents With Autism Spectrum Disorder in Japan: A Randomized, Double‐Blind, Placebo‐Controlled Study,” Child Psychiatry and Human Development 48, no. 5 (2017): 796–806, https://doi.org/10.1007/s10578‐016‐0704‐x.
R. N. Marcus, R. Owen, L. Kamen, et al., “A Placebo‐Controlled, Fixed‐Dose Study of Aripiprazole in Children and Adolescents With Irritability Associated With Autistic Disorder,” Journal of the American Academy of Child and Adolescent Psychiatry 48 (2009): 1110–1119.
R. Owen, L. Sikich, R. N. Marcus, et al., “Aripiprazole in the Treatment of Irritability in Children and Adolescents With Autistic Disorder,” Pediatrics 124, no. 6 (2009): 1533–1540, https://doi.org/10.1542/peds.2008‐3782.
J. T. McCracken, J. McGough, B. Shah, et al., “Risperidone in Children With Autism and Serious Behavioral Problems,” New England Journal of Medicine 347, no. 5 (2002): 314–321, https://doi.org/10.1056/NEJMoa013171.
S. A. Montgomery and M. A. Åsberg, “A New Depression Scale Designed to Be Sensitive to Change,” British Journal of Psychiatry 134, no. 4 (1979): 382–389.
G. S. Sachs, P. P. Yeung, L. Rekeda, A. Khan, J. L. Adams, and M. Fava, “Adjunctive Cariprazine for the Treatment of Patients With Major Depressive Disorder: A Randomized, Double‐Blind, Placebo‐Controlled Phase 3 Study,” American Journal of Psychiatry 180, no. 3 (2023): 241–251.
R. Riesenberg, P. P. Yeung, L. Rekeda, G. S. Sachs, M. Kerolous, and M. Fava, “Cariprazine for the Adjunctive Treatment of Major Depressive Disorder in Patients With Inadequate Response to Antidepressant Therapy: Results of a Randomized, Double‐Blind, Placebo‐Controlled Study,” Journal of Clinical Psychiatry 84, no. 5 (2023): 48439.
S. Durgam, W. Earley, H. Guo, et al., “Efficacy and Safety of Adjunctive Cariprazine in Inadequate Responders to Antidepressants: A Randomized, Double‐Blind, Placebo‐Controlled Study in Adult Patients With Major Depressive Disorder,” Journal of Clinical Psychiatry 77, no. 3 (2016): 6112.
R. E. Kass and A. E. Raftery, “Bayes Factors,” Journal of the American Statistical Association 90, no. 430 (1995): 773–795.
J. G. Ibrahim and M.‐H. Chen, “Power Prior Distributions for Regression Models,” Statistical Science 15, no. 1 (2000): 46–60.
B. P. Hobbs, D. J. Sargent, and B. P. Carlin, “Commensurate Priors for Incorporating Historical Information in Clinical Trials Using General and Generalized Linear Models,” Bayesian Analysis 7, no. 3 (2012): 639–674.
B. Neuenschwander, S. Weber, H. Schmidli, and A. O'Hagan, “Predictively Consistent Prior Effective Sample Sizes,” Biometrics 76, no. 2 (2020): 578–587.
T. Friede and M. Kieser, “Sample Size Recalculation in Internal Pilot Study Designs: A Review,” Biometrical Journal 48, no. 4 (2006): 537–555.
Grant Information:
AbbVie
Contributed Indexing:
Keywords: MAP priors; adaptive Bayesian borrowing; blinded sample size re‐estimation; clinical trials; leveraging historical control
Entry Date(s):
Date Created: 20251210 Date Completed: 20251210 Latest Revision: 20251210
Update Code:
20251210
DOI:
10.1002/pst.70051
PMID:
41369947
Database:
MEDLINE
Weitere Informationen
In clinical research, it is increasingly difficult to conduct fully powered and well-balanced randomized controlled trials, particularly when studying rare or devastating diseases and pediatric patients. While Bayesian methodologies are very useful for leveraging historical control data to meet some of these challenges, many practical and statistical concerns emerge when prospectively specifying a design to implement Bayesian methods. In this article, we discuss these concerns and propose novel methods to ensure statistical rigor when applying Bayesian methodology. A novel adaptive Bayesian borrowing (ABB) method proposed here borrows from historical control data to increase the precision of the control arm based on the observed congruence of the historical and current data. The method would also enable an adaptive increase of sample size to accommodate accumulating information. We demonstrate that this approach can be prospectively specified and provides a statistically rigorous and transparent inference while mitigating the risk of potential conflict between historical and current control data, as well as misspecifications of variability in the endpoints.
(© 2025 John Wiley & Sons Ltd.)