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Robust meta-analytic-predictive priors in clinical trials with historical control information

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  • Heinz Schmidli
  • Sandro Gsteiger
  • Satrajit Roychoudhury
  • Anthony O'Hagan
  • David Spiegelhalter
  • Beat Neuenschwander

Abstract

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Suggested Citation

  • Heinz Schmidli & Sandro Gsteiger & Satrajit Roychoudhury & Anthony O'Hagan & David Spiegelhalter & Beat Neuenschwander, 2014. "Robust meta-analytic-predictive priors in clinical trials with historical control information," Biometrics, The International Biometric Society, vol. 70(4), pages 1023-1032, December.
    • Handle: RePEc:bla:biomet:v:70:y:2014:i:4:p:1023-1032
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    File URL: http://hdl.handle.net/10.1111/biom.12242
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    References listed on IDEAS

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    1. Lorenzo Trippa & Gary L. Rosner & Peter Müller, 2012. "Bayesian Enrichment Strategies for Randomized Discontinuation Trials," Biometrics, The International Biometric Society, vol. 68(1), pages 203-211, March.
    2. Brian P. Hobbs & Bradley P. Carlin & Sumithra J. Mandrekar & Daniel J. Sargent, 2011. "Hierarchical Commensurate and Power Prior Models for Adaptive Incorporation of Historical Information in Clinical Trials," Biometrics, The International Biometric Society, vol. 67(3), pages 1047-1056, September.
    3. Satoshi Morita & Peter F. Thall & Peter Müller, 2008. "Determining the Effective Sample Size of a Parametric Prior," Biometrics, The International Biometric Society, vol. 64(2), pages 595-602, June.
    4. L. G. Leon-Novelo & B. Nebiyou Bekele & P. Müller & F. Quintana & K. Wathen, 2012. "Borrowing Strength with Nonexchangeable Priors over Subpopulations," Biometrics, The International Biometric Society, vol. 68(2), pages 550-558, June.
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    Cited by:

    1. Chenghao Chu & Bingming Yi, 2021. "Dynamic historical data borrowing using weighted average," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1259-1280, November.
    2. Egidi, Leonardo, 2022. "Effective sample size for a mixture prior," Statistics & Probability Letters, Elsevier, vol. 183(C).
    3. Agovino, Massimiliano & Cerciello, Massimiliano & Musella, Gaetano, 2021. "Campania and cancer mortality: An inseparable pair? The role of environmental quality and socio-economic deprivation," Social Science & Medicine, Elsevier, vol. 287(C).
    4. Qingyang Liu & Junxian Geng & Frank Fleischer & Qiqi Deng, 2022. "Efficacy-Driven Dose Finding with Toxicity Control in Phase I Oncology Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 413-431, December.
    5. Dan J. Spitzner, 2023. "Calibrated Bayes factors under flexible priors," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 733-767, September.
    6. Arnaud Monseur & Bradley P. Carlin & Bruno Boulanger & Andreea Seferian & Laurent Servais & Chris Freitag & Leen Thielemans, 2022. "Leveraging Natural History Data in One- and Two-Arm Hierarchical Bayesian Studies of Rare Disease Progression," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(2), pages 237-258, July.
    7. David Kaplan & Jianshen Chen & Sinan Yavuz & Weicong Lyu, 2023. "Bayesian Dynamic Borrowing of Historical Information with Applications to the Analysis of Large-Scale Assessments," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 1-30, March.
    8. Jingjing Ye & Gregory Reaman, 2022. "Improving Early Futility Determination by Learning from External Data in Pediatric Cancer Clinical Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(2), pages 337-351, July.
    9. Wenlin Yuan & Ming-Hui Chen & John Zhong, 2022. "Flexible Conditional Borrowing Approaches for Leveraging Historical Data in the Bayesian Design of Superiority Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(2), pages 197-215, July.
    10. Lanju Zhang & Zailong Wang & Li Wang & Lu Cui & Jeremy Sokolove & Ivan Chan, 2022. "A Simple Approach to Incorporating Historical Control Data in Clinical Trial Design and Analysis," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(2), pages 216-236, July.
    11. Leonhard Held & Rafael Sauter, 2017. "Adaptive prior weighting in generalized regression," Biometrics, The International Biometric Society, vol. 73(1), pages 242-251, March.
    12. Schmidli, Heinz & Neuenschwander, Beat & Friede, Tim, 2017. "Meta-analytic-predictive use of historical variance data for the design and analysis of clinical trials," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 100-110.
    13. Ian Wadsworth & Lisa V. Hampson & Thomas Jaki & Graeme J. Sills & Anthony G. Marson & Richard Appleton, 2020. "A quantitative framework to inform extrapolation decisions in children," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 515-534, February.
    14. Gerber, Florian & Gsponer, Thomas, 2016. "gsbDesign: An R Package for Evaluating the Operating Characteristics of a Group Sequential Bayesian Design," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(i11).
    15. Beat Neuenschwander & Sebastian Weber & Heinz Schmidli & Anthony O'Hagan, 2020. "Predictively consistent prior effective sample sizes," Biometrics, The International Biometric Society, vol. 76(2), pages 578-587, June.
    16. Moreno Ursino & Nigel Stallard, 2021. "Bayesian Approaches for Confirmatory Trials in Rare Diseases: Opportunities and Challenges," IJERPH, MDPI, vol. 18(3), pages 1-9, January.
    17. Danila Azzolina & Giulia Lorenzoni & Silvia Bressan & Liviana Da Dalt & Ileana Baldi & Dario Gregori, 2021. "Handling Poor Accrual in Pediatric Trials: A Simulation Study Using a Bayesian Approach," IJERPH, MDPI, vol. 18(4), pages 1-16, February.

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