IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i4p2095-d503201.html
   My bibliography  Save this article

Handling Poor Accrual in Pediatric Trials: A Simulation Study Using a Bayesian Approach

Author

Listed:
  • Danila Azzolina

    (Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, 35128 Padova, Italy
    Department of Translational Medicine, University of Eastern Piedmont, 28100 Novara, Italy)

  • Giulia Lorenzoni

    (Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, 35128 Padova, Italy)

  • Silvia Bressan

    (Department of Women’s and Children’s Health, University of Padova, 35128 Padova, Italy)

  • Liviana Da Dalt

    (Department of Women’s and Children’s Health, University of Padova, 35128 Padova, Italy)

  • Ileana Baldi

    (Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, 35128 Padova, Italy)

  • Dario Gregori

    (Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, 35128 Padova, Italy)

Abstract

In the conduction of trials, a common situation is related to potential difficulties in recruiting the planned sample size as provided by the study design. A Bayesian analysis of such trials might provide a framework to combine prior evidence with current evidence, and it is an accepted approach by regulatory agencies. However, especially for small trials, the Bayesian inference may be severely conditioned by the prior choices. The Renal Scarring Urinary Infection (RESCUE) trial, a pediatric trial that was a candidate for early termination due to underrecruitment, served as a motivating example to investigate the effects of the prior choices on small trial inference. The trial outcomes were simulated by assuming 50 scenarios combining different sample sizes and true absolute risk reduction (ARR). The simulated data were analyzed via the Bayesian approach using 0%, 50%, and 100% discounting factors on the beta power prior. An informative inference (0% discounting) on small samples could generate data-insensitive results. Instead, the 50% discounting factor ensured that the probability of confirming the trial outcome was higher than 80%, but only for an ARR higher than 0.17. A suitable option to maintain data relevant to the trial inference is to define a discounting factor based on the prior parameters. Nevertheless, a sensitivity analysis of the prior choices is highly recommended.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:4:p:2095-:d:503201
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/4/2095/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/4/2095/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Danila Azzolina & Paola Berchialla & Dario Gregori & Ileana Baldi, 2021. "Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review," IJERPH, MDPI, vol. 18(4), pages 1-21, February.
    3. Stavros Nikolakopoulos & Ingeborg van der Tweel & Kit C. B. Roes, 2018. "Dynamic borrowing through empirical power priors that control type I error," Biometrics, The International Biometric Society, vol. 74(3), pages 874-880, September.
    4. De Santis, Fulvio, 2006. "Power Priors and Their Use in Clinical Trials," The American Statistician, American Statistical Association, vol. 60, pages 122-129, May.
    5. John Geweke, 1991. "Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments," Staff Report 148, Federal Reserve Bank of Minneapolis.
    6. 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.
    7. David J. Spiegelhalter & Laurence S. Freedman & Mahesh K. B. Parmar, 1994. "Bayesian Approaches to Randomized Trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 157(3), pages 357-387, May.
    8. Fulvio De Santis, 2007. "Using historical data for Bayesian sample size determination," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(1), pages 95-113, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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.
    4. Ming-Hui Chen & Joseph G. Ibrahim & Peter Lam & Alan Yu & Yuanye Zhang, 2011. "Bayesian Design of Noninferiority Trials for Medical Devices Using Historical Data," Biometrics, The International Biometric Society, vol. 67(3), pages 1163-1170, September.
    5. 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.
    6. 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.
    7. 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.
    8. Chen, Nan & Carlin, Bradley P. & Hobbs, Brian P., 2018. "Web-based statistical tools for the analysis and design of clinical trials that incorporate historical controls," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 50-68.
    9. 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.
    10. Jing Zhang & Yunzhi Kong & A. John Bailer & Zheng Zhu & Byran Smucker, 2022. "Incorporating Historical Data When Determining Sample Size Requirements for Aquatic Toxicity Experiments," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(3), pages 544-561, September.
    11. 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.
    12. Buddhavarapu, Prasad & Bansal, Prateek & Prozzi, Jorge A., 2021. "A new spatial count data model with time-varying parameters," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 566-586.
    13. Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez, 2001. "Comparing dynamic equilibrium economies to data," FRB Atlanta Working Paper 2001-23, Federal Reserve Bank of Atlanta.
    14. repec:col:000416:018560 is not listed on IDEAS
    15. Bai, Yizhou & Xue, Cheng, 2021. "An empirical study on the regulated Chinese agricultural commodity futures market based on skew Ornstein-Uhlenbeck model," Research in International Business and Finance, Elsevier, vol. 57(C).
    16. Peng Yang & Yuansong Zhao & Lei Nie & Jonathon Vallejo & Ying Yuan, 2023. "SAM: Self‐adapting mixture prior to dynamically borrow information from historical data in clinical trials," Biometrics, The International Biometric Society, vol. 79(4), pages 2857-2868, December.
    17. 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.
    18. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2012. "The directional identification problem in Bayesian factor analysis: An ex-post approach," Kiel Working Papers 1799, Kiel Institute for the World Economy (IfW Kiel).
    19. Thomas A. Murray & Brian P. Hobbs & Theodore C. Lystig & Bradley P. Carlin, 2014. "Semiparametric Bayesian commensurate survival model for post-market medical device surveillance with non-exchangeable historical data," Biometrics, The International Biometric Society, vol. 70(1), pages 185-191, March.
    20. Francesco De Pretis & Barbara Osimani, 2019. "New Insights in Computational Methods for Pharmacovigilance: E-Synthesis , a Bayesian Framework for Causal Assessment," IJERPH, MDPI, vol. 16(12), pages 1-19, June.
    21. Michael T. Owyang, 2002. "Modeling Volcker as a non-absorbing state: agnostic identification of a Markov-switching VAR," Working Papers 2002-018, Federal Reserve Bank of St. Louis.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:4:p:2095-:d:503201. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.