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Article: Empirical Bayes Gaussian likelihood estimation of exposure distributions from pooled samples in human biomonitoring

TitleEmpirical Bayes Gaussian likelihood estimation of exposure distributions from pooled samples in human biomonitoring
Authors
KeywordsBias Correction
Biomonitoring
Empirical Bayes Estimation
Gaussian Likelihood
Lognormal Distribution
Pooled Samples
Issue Date2014
PublisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/
Citation
Statistics in Medicine, 2014, v. 33, p. 4999-5014 How to Cite?
AbstractHuman biomonitoring of exposure to environmental chemicals is important. Individual monitoring is not viable because of low individual exposure level or insufficient volume of materials and the prohibitive cost of taking measurements from many subjects. Pooling of samples is an efficient and cost‐effective way to collect data. Estimation is, however, complicated as individual values within each pool are not observed but are only known up to their average or weighted average. The distribution of such averages is intractable when the individual measurements are lognormally distributed, which is a common assumption. We propose to replace the intractable distribution of the pool averages by a Gaussian likelihood to obtain parameter estimates. If the pool size is large, this method produces statistically efficient estimates, but regardless of pool size, the method yields consistent estimates as the number of pools increases. An empirical Bayes (EB) Gaussian likelihood approach, as well as its Bayesian analog, is developed to pool information from various demographic groups by using a mixed‐effect formulation. We also discuss methods to estimate the underlying mean–variance relationship and to select a good model for the means, which can be incorporated into the proposed EB or Bayes framework. By borrowing strength across groups, the EB estimator is more efficient than the individual group‐specific estimator. Simulation results show that the EB Gaussian likelihood estimates outperform a previous method proposed for the National Health and Nutrition Examination Surveys with much smaller bias and better coverage in interval estimation, especially after correction of bias. Copyright © 2014 John Wiley & Sons, Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/221664
ISSN
2015 Impact Factor: 1.533
2015 SCImago Journal Rankings: 1.811
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, X-
dc.contributor.authorKuk, AYC.-
dc.contributor.authorXu, J-
dc.date.accessioned2015-12-04T15:28:58Z-
dc.date.available2015-12-04T15:28:58Z-
dc.date.issued2014-
dc.identifier.citationStatistics in Medicine, 2014, v. 33, p. 4999-5014-
dc.identifier.issn0277-6715-
dc.identifier.urihttp://hdl.handle.net/10722/221664-
dc.description.abstractHuman biomonitoring of exposure to environmental chemicals is important. Individual monitoring is not viable because of low individual exposure level or insufficient volume of materials and the prohibitive cost of taking measurements from many subjects. Pooling of samples is an efficient and cost‐effective way to collect data. Estimation is, however, complicated as individual values within each pool are not observed but are only known up to their average or weighted average. The distribution of such averages is intractable when the individual measurements are lognormally distributed, which is a common assumption. We propose to replace the intractable distribution of the pool averages by a Gaussian likelihood to obtain parameter estimates. If the pool size is large, this method produces statistically efficient estimates, but regardless of pool size, the method yields consistent estimates as the number of pools increases. An empirical Bayes (EB) Gaussian likelihood approach, as well as its Bayesian analog, is developed to pool information from various demographic groups by using a mixed‐effect formulation. We also discuss methods to estimate the underlying mean–variance relationship and to select a good model for the means, which can be incorporated into the proposed EB or Bayes framework. By borrowing strength across groups, the EB estimator is more efficient than the individual group‐specific estimator. Simulation results show that the EB Gaussian likelihood estimates outperform a previous method proposed for the National Health and Nutrition Examination Surveys with much smaller bias and better coverage in interval estimation, especially after correction of bias. Copyright © 2014 John Wiley & Sons, Ltd.-
dc.languageeng-
dc.publisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/-
dc.relation.ispartofStatistics in Medicine-
dc.subjectBias Correction-
dc.subjectBiomonitoring-
dc.subjectEmpirical Bayes Estimation-
dc.subjectGaussian Likelihood-
dc.subjectLognormal Distribution-
dc.subjectPooled Samples-
dc.titleEmpirical Bayes Gaussian likelihood estimation of exposure distributions from pooled samples in human biomonitoring-
dc.typeArticle-
dc.identifier.emailXu, J: xujf@hku.hk-
dc.identifier.authorityXu, J=rp02086-
dc.identifier.doi10.1002/sim.6304-
dc.identifier.pmid25213192-
dc.identifier.scopuseid_2-s2.0-84908679555-
dc.identifier.volume33-
dc.identifier.spage4999-
dc.identifier.epage5014-
dc.identifier.isiWOS:000345016700011-

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