Evidence of Absence: Bayesian Way to Reveal True Zeros Among Occupational Exposures.
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| Title: | Evidence of Absence: Bayesian Way to Reveal True Zeros Among Occupational Exposures. |
|---|---|
| Authors: | Lavoue, Jerome1, Burstyn, Igor2 ib68@drexel.edu |
| Source: | Annals of Work Exposures & Health. Jan2021, Vol. 65 Issue 1, p84-95. 12p. |
| Subjects: | Research methodology, Risk assessment, Statistics, Data analysis, Statistical models, Descriptive statistics, Occupational exposure |
| Abstract: | Objectives Workplace exposure measurements typically contain some observations below limit of detection. The current paradigm for exposure data interpretation relies on the lognormal distribution, where censored observation are assumed to be present but not quantifiable. However, there are setting were such assumptions are untenable and true zero exposures cannot be ruled out. This issue can be non-trivial because decisions about compliance depend on the adequacy of the lognormal model. Methods We adapted previously described statistical models for mixture of true zeros and lognormal distribution to function within Bayesian procedure that overcomes historical limitations that precluded them from being used in practice. We compared the performance of the new models and the traditional lognormal model in simulation. Their implementation is illustrated in diverse datasets. Results The approach we propose involves estimating the proportion of true zeroes, and the geometric mean and standard deviation of the lognormal component of the mixture. This can be implemented in practice either based on the truncated lognormal model fit to the observed data, or on the censored Bernoulli-lognormal mixture model, which has the advantage of allowing for multiple censoring points. Both models can be implemented via a free online application. In simulations, when none of the censored values were zeros, all estimation procedures led to similar risk assessment. However, when all or most of the censored values were zeros, the traditional approach that assumes lognormal distribution performed noticeably worse than newly proposed methods, typically overestimating noncompliance. Application to real data suggests that we cannot rule out presence of true zero exposures in typical measurement series gathered by occupational hygienists. Conclusions Forcing the usual lognormal model to data containing a large proportion of censored values can bias risk assessment if a substantial part of the censored points are true zeroes. The Bernoulli-lognormal model is a suitable and accessible model that can account for such challenging data, and leads to unbiased risk assessments regardless of the presence of true zeros in the data. [ABSTRACT FROM AUTHOR] |
| Copyright of Annals of Work Exposures & Health is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 148168773 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Evidence of Absence: Bayesian Way to Reveal True Zeros Among Occupational Exposures. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lavoue%2C+Jerome%22">Lavoue, Jerome</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Burstyn%2C+Igor%22">Burstyn, Igor</searchLink><relatesTo>2</relatesTo><i> ib68@drexel.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Annals+of+Work+Exposures+%26+Health%22">Annals of Work Exposures & Health</searchLink>. Jan2021, Vol. 65 Issue 1, p84-95. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Research+methodology%22">Research methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Occupational+exposure%22">Occupational exposure</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Objectives Workplace exposure measurements typically contain some observations below limit of detection. The current paradigm for exposure data interpretation relies on the lognormal distribution, where censored observation are assumed to be present but not quantifiable. However, there are setting were such assumptions are untenable and true zero exposures cannot be ruled out. This issue can be non-trivial because decisions about compliance depend on the adequacy of the lognormal model. Methods We adapted previously described statistical models for mixture of true zeros and lognormal distribution to function within Bayesian procedure that overcomes historical limitations that precluded them from being used in practice. We compared the performance of the new models and the traditional lognormal model in simulation. Their implementation is illustrated in diverse datasets. Results The approach we propose involves estimating the proportion of true zeroes, and the geometric mean and standard deviation of the lognormal component of the mixture. This can be implemented in practice either based on the truncated lognormal model fit to the observed data, or on the censored Bernoulli-lognormal mixture model, which has the advantage of allowing for multiple censoring points. Both models can be implemented via a free online application. In simulations, when none of the censored values were zeros, all estimation procedures led to similar risk assessment. However, when all or most of the censored values were zeros, the traditional approach that assumes lognormal distribution performed noticeably worse than newly proposed methods, typically overestimating noncompliance. Application to real data suggests that we cannot rule out presence of true zero exposures in typical measurement series gathered by occupational hygienists. Conclusions Forcing the usual lognormal model to data containing a large proportion of censored values can bias risk assessment if a substantial part of the censored points are true zeroes. The Bernoulli-lognormal model is a suitable and accessible model that can account for such challenging data, and leads to unbiased risk assessments regardless of the presence of true zeros in the data. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Annals of Work Exposures & Health is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1093/annweh/wxaa086 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 84 Subjects: – SubjectFull: Research methodology Type: general – SubjectFull: Risk assessment Type: general – SubjectFull: Statistics Type: general – SubjectFull: Data analysis Type: general – SubjectFull: Statistical models Type: general – SubjectFull: Descriptive statistics Type: general – SubjectFull: Occupational exposure Type: general Titles: – TitleFull: Evidence of Absence: Bayesian Way to Reveal True Zeros Among Occupational Exposures. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lavoue, Jerome – PersonEntity: Name: NameFull: Burstyn, Igor IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 23987308 Numbering: – Type: volume Value: 65 – Type: issue Value: 1 Titles: – TitleFull: Annals of Work Exposures & Health Type: main |
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