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Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review

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  • 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 Traslational Medicine, University of Eastern Piedmont, 28100 Novara, Italy)

  • Paola Berchialla

    (Department of Clinical and Biological Science, University of Turin, 10124 Turin, 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)

  • Ileana Baldi

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

Abstract

Bayesian inference is increasingly popular in clinical trial design and analysis. The subjective knowledge derived from an expert elicitation procedure may be useful to define a prior probability distribution when no or limited data is available. This work aims to investigate the state-of-the-art Bayesian prior elicitation methods with a focus on clinical trial research. A literature search on the Current Index to Statistics (CIS), PubMed, and Web of Science (WOS) databases, considering “prior elicitation” as a search string, was run on 1 November 2020. Summary statistics and trend of publications over time were reported. Finally, a Latent Dirichlet Allocation (LDA) model was developed to recognise latent topics in the pertinent papers retrieved. A total of 460 documents pertinent to the Bayesian prior elicitation were identified. Of these, 213 (45.4%) were published in the “Probability and Statistics” area. A total of 42 articles pertain to clinical trial and the majority of them (81%) reports parametric techniques as elicitation method. The last decade has seen an increased interest in prior elicitation and the gap between theory and application getting narrower and narrower. Given the promising flexibility of non-parametric approaches to the experts’ elicitation, more efforts are needed to ensure their diffusion also in applied settings.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:4:p:1833-:d:498998
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    References listed on IDEAS

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    1. Grün, Bettina & Hornik, Kurt, 2011. "topicmodels: An R Package for Fitting Topic Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i13).
    2. Ying Kuen Cheung, 2002. "On the Use of Nonparametric Curves in Phase I Trials with Low Toxicity Tolerance," Biometrics, The International Biometric Society, vol. 58(1), pages 237-240, March.
    3. Rita Esther Zapata-V�zquez & Anthony O'Hagan & Leonardo Soares Bastos, 2014. "Eliciting expert judgements about a set of proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(9), pages 1919-1933, 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. Jeremy E. Oakley & Anthony O'Hagan, 2007. "Uncertainty in prior elicitations: a nonparametric approach," Biometrika, Biometrika Trust, vol. 94(2), pages 427-441.
    6. Bekele, B. Nebiyou & Thall, Peter F., 2004. "Dose-Finding Based on Multiple Toxicities in a Soft Tissue Sarcoma Trial," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 26-35, January.
    7. Bornkamp, Björn & Ickstadt, Katja, 2009. "A Note on B-Splines for Semiparametric Elicitation," The American Statistician, American Statistical Association, vol. 63(4), pages 373-377.
    8. Matthew W. Wheeler & Walter W. Piegorsch & Albert John Bailer, 2019. "Quantal Risk Assessment Database: A Database for Exploring Patterns in Quantal Dose‐Response Data in Risk Assessment and its Application to Develop Priors for Bayesian Dose‐Response Analysis," Risk Analysis, John Wiley & Sons, vol. 39(3), pages 616-629, March.
    9. Ruitao Lin & Peter F. Thall & Ying Yuan, 2020. "An adaptive trial design to optimize dose‐schedule regimes with delayed outcomes," Biometrics, The International Biometric Society, vol. 76(1), pages 304-315, March.
    10. Leonhard Held & Rafael Sauter, 2017. "Adaptive prior weighting in generalized regression," Biometrics, The International Biometric Society, vol. 73(1), pages 242-251, March.
    11. Garthwaite, Paul H. & Kadane, Joseph B. & O'Hagan, Anthony, 2005. "Statistical Methods for Eliciting Probability Distributions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 680-701, June.
    12. Rebecca M. Turner & David J. Spiegelhalter & Gordon C. S. Smith & Simon G. Thompson, 2009. "Bias modelling in evidence synthesis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 21-47, January.
    13. Manuel Wiesenfarth & Silvia Calderazzo, 2020. "Quantification of prior impact in terms of effective current sample size," Biometrics, The International Biometric Society, vol. 76(1), pages 326-336, March.
    14. Donald L. Keefer, 1994. "Certainty Equivalents for Three-Point Discrete-Distribution Approximations," Management Science, INFORMS, vol. 40(6), pages 760-773, June.
    15. Ali E. Abbas & David V. Budescu & Hsiu-Ting Yu & Ryan Haggerty, 2008. "A Comparison of Two Probability Encoding Methods: Fixed Probability vs. Fixed Variable Values," Decision Analysis, INFORMS, vol. 5(4), pages 190-202, December.
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    2. 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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