Document Detail


A mixture model for occupational exposure mean testing with a limit of detection.
MedLine Citation:
PMID:  11550915     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Information from detectable exposure measurements randomly sampled from a left-truncated log-normal distribution may be used to evaluate the distribution of nondetectable values that fall below an analytic limit of detection. If the proportion of nondetects is larger than expected under log normality, alternative models to account for these unobserved data should be considered. We discuss one such model that incorporates a mixture of true zero exposures and a log-normal distribution with possible left censoring, previously considered in a different context by Moulton and Halsey (1995, Biometrics 51, 1570-1578). A particular relationship is demonstrated between maximum likelihood parameter estimates based on this mixture model and those assuming either left-truncated or left-censored data. These results emphasize the need for caution when choosing a model to fit data involving nondetectable values. A one-sided likelihood ratio test for comparing mean exposure under the mixture model to an occupational exposure limit is then developed and evaluated via simulations. An example demonstrates the potential impact of specifying an incorrect model for the nondetectable values.
Authors:
D J Taylor; L L Kupper; S M Rappaport; R H Lyles
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Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, U.S. Gov't, P.H.S.    
Journal Detail:
Title:  Biometrics     Volume:  57     ISSN:  0006-341X     ISO Abbreviation:  Biometrics     Publication Date:  2001 Sep 
Date Detail:
Created Date:  2001-09-11     Completed Date:  2002-02-25     Revised Date:  2007-11-14    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  681-8     Citation Subset:  IM    
Affiliation:
Family Health International, Durham, North Carolina 27713, USA. dtaylor@fhi.org
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Benzene / adverse effects
Biometry*
Humans
Likelihood Functions
Models, Statistical
Occupational Exposure* / statistics & numerical data
Grant Support
ID/Acronym/Agency:
5-T32-ES07018/ES/NIEHS NIH HHS
Chemical
Reg. No./Substance:
71-43-2/Benzene

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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