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Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis
Suppose we have a Bayesian model that combines evidence from several different sources. We want to know which model parameters most affect the estimate or decision from the model, or which of the parameter uncertainties drive the decision uncertainty. Furthermore, we want to prioritize what further...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7034331/ https://www.ncbi.nlm.nih.gov/pubmed/32165869 http://dx.doi.org/10.1080/01621459.2018.1562932 |
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author | Jackson, Christopher Presanis, Anne Conti, Stefano De Angelis, Daniela |
author_facet | Jackson, Christopher Presanis, Anne Conti, Stefano De Angelis, Daniela |
author_sort | Jackson, Christopher |
collection | PubMed |
description | Suppose we have a Bayesian model that combines evidence from several different sources. We want to know which model parameters most affect the estimate or decision from the model, or which of the parameter uncertainties drive the decision uncertainty. Furthermore, we want to prioritize what further data should be collected. These questions can be addressed by Value of Information (VoI) analysis, in which we estimate expected reductions in loss from learning specific parameters or collecting data of a given design. We describe the theory and practice of VoI for Bayesian evidence synthesis, using and extending ideas from health economics, computer modeling and Bayesian design. The methods are general to a range of decision problems including point estimation and choices between discrete actions. We apply them to a model for estimating prevalence of HIV infection, combining indirect information from surveys, registers, and expert beliefs. This analysis shows which parameters contribute most of the uncertainty about each prevalence estimate, and the expected improvements in precision from specific amounts of additional data. These benefits can be traded with the costs of sampling to determine an optimal sample size. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. |
format | Online Article Text |
id | pubmed-7034331 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-70343312020-03-10 Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis Jackson, Christopher Presanis, Anne Conti, Stefano De Angelis, Daniela J Am Stat Assoc Applications and Case Studies Suppose we have a Bayesian model that combines evidence from several different sources. We want to know which model parameters most affect the estimate or decision from the model, or which of the parameter uncertainties drive the decision uncertainty. Furthermore, we want to prioritize what further data should be collected. These questions can be addressed by Value of Information (VoI) analysis, in which we estimate expected reductions in loss from learning specific parameters or collecting data of a given design. We describe the theory and practice of VoI for Bayesian evidence synthesis, using and extending ideas from health economics, computer modeling and Bayesian design. The methods are general to a range of decision problems including point estimation and choices between discrete actions. We apply them to a model for estimating prevalence of HIV infection, combining indirect information from surveys, registers, and expert beliefs. This analysis shows which parameters contribute most of the uncertainty about each prevalence estimate, and the expected improvements in precision from specific amounts of additional data. These benefits can be traded with the costs of sampling to determine an optimal sample size. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. Taylor & Francis 2019-04-30 /pmc/articles/PMC7034331/ /pubmed/32165869 http://dx.doi.org/10.1080/01621459.2018.1562932 Text en © 2019 American Statistical Association https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications and Case Studies Jackson, Christopher Presanis, Anne Conti, Stefano De Angelis, Daniela Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis |
title | Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis |
title_full | Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis |
title_fullStr | Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis |
title_full_unstemmed | Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis |
title_short | Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis |
title_sort | value of information: sensitivity analysis and research design in bayesian evidence synthesis |
topic | Applications and Case Studies |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7034331/ https://www.ncbi.nlm.nih.gov/pubmed/32165869 http://dx.doi.org/10.1080/01621459.2018.1562932 |
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