Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review
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 Ba...
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doaj-8f1a00f5714f4a8888f80365468031a62021-02-14T00:03:41ZengMDPI AGInternational Journal of Environmental Research and Public Health1661-78271660-46012021-02-01181833183310.3390/ijerph18041833Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature ReviewDanila Azzolina0Paola Berchialla1Dario Gregori2Ileana Baldi3Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, Padova 35128, ItalyDepartment of Clinical and Biological Science, University of Turin, Turin 10124, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, Padova 35128, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, Padova 35128, ItalyBayesian 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.https://www.mdpi.com/1660-4601/18/4/1833prior elicitationlatent dirichlet allocationclinical trial |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Danila Azzolina Paola Berchialla Dario Gregori Ileana Baldi |
spellingShingle |
Danila Azzolina Paola Berchialla Dario Gregori Ileana Baldi Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review International Journal of Environmental Research and Public Health prior elicitation latent dirichlet allocation clinical trial |
author_facet |
Danila Azzolina Paola Berchialla Dario Gregori Ileana Baldi |
author_sort |
Danila Azzolina |
title |
Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review |
title_short |
Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review |
title_full |
Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review |
title_fullStr |
Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review |
title_full_unstemmed |
Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review |
title_sort |
prior elicitation for use in clinical trial design and analysis: a literature review |
publisher |
MDPI AG |
series |
International Journal of Environmental Research and Public Health |
issn |
1661-7827 1660-4601 |
publishDate |
2021-02-01 |
description |
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. |
topic |
prior elicitation latent dirichlet allocation clinical trial |
url |
https://www.mdpi.com/1660-4601/18/4/1833 |
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