A two-stage random-effects estimator for meta-analyses of the value per statistical life
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| Publicat a: | PLoS One vol. 20, no. 6 (Jun 2025), p. e0324630 |
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| Autor principal: | |
| Altres autors: | , , , |
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Public Library of Science
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| Accés en línia: | Citation/Abstract Full Text Full Text - PDF |
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| 001 | 3218648222 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0324630 |2 doi | |
| 035 | |a 3218648222 | ||
| 045 | 2 | |b d20250601 |b d20250630 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Newbold, Stephen C | |
| 245 | 1 | |a A two-stage random-effects estimator for meta-analyses of the value per statistical life | |
| 260 | |b Public Library of Science |c Jun 2025 | ||
| 513 | |a Evidence Based Healthcare Journal Article | ||
| 520 | 3 | |a We developed and examined the performance of a two-stage random-effects meta-analysis estimator for synthesizing published estimates of the value per statistical life (VSL). The meta-estimation approach accommodates unbalanced panels with one or multiple observations from each independent group of primary estimates, and distinguishes between sampling and non-sampling sources of error, both within and between groups. We used Monte Carlo simulation experiments to test the performance of the meta-estimator on constructed datasets. Simulation results indicate that, when applied to datasets of modest size, the approach performs best when the within-group non-sampling error variances are assumed to be homogeneous among groups. This allows for two levels of non-sampling errors while preserving degrees of freedom and therefore increasing statistical efficiency. Simulation results also show that the estimator compares favorably to several other commonly used meta-analysis estimators, including other two-stage estimators. As a demonstration, we applied the approach to a pre-existing meta-dataset including 113 VSL estimates assembled from 10 revealed preference and 9 stated preference studies conducted in the U.S. and published between 1999 and 2019. | |
| 610 | 4 | |a Environmental Protection Agency--EPA | |
| 651 | 4 | |a United States--US | |
| 653 | |a Statistics | ||
| 653 | |a Datasets | ||
| 653 | |a Estimates | ||
| 653 | |a Air pollution | ||
| 653 | |a Estimators | ||
| 653 | |a Outdoor air quality | ||
| 653 | |a Sampling | ||
| 653 | |a Bias | ||
| 653 | |a COVID-19 | ||
| 653 | |a Monte Carlo simulation | ||
| 653 | |a Research methodology | ||
| 653 | |a Fatalities | ||
| 653 | |a Public health | ||
| 653 | |a Meta-analysis | ||
| 653 | |a Environmental protection | ||
| 653 | |a Public policy | ||
| 653 | |a Social | ||
| 700 | 1 | |a Dockins, Chris | |
| 700 | 1 | |a Simon, Nathalie | |
| 700 | 1 | |a Maguire, Kelly | |
| 700 | 1 | |a Abdullah Muhammad Sakib | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 6 (Jun 2025), p. e0324630 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3218648222/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3218648222/fulltext/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3218648222/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |