Estimation methods with ordered exposure subject to measurement error and missingness in semi-ecological design
In epidemiological studies, it is often not possible to measure accurately exposures of participants even if their response variable can be measured without error. When there are several groups of subjects, occupational epidemiologists employ group-based strategy (GBS) for exposure assessment to red...
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| Published in | BMC medical research methodology Vol. 12; no. 1; p. 135 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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England
BioMed Central Ltd
04.09.2012
BioMed Central BMC |
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| Abstract | In epidemiological studies, it is often not possible to measure accurately exposures of participants even if their response variable can be measured without error. When there are several groups of subjects, occupational epidemiologists employ group-based strategy (GBS) for exposure assessment to reduce bias due to measurement errors: individuals of a group/job within study sample are assigned commonly to the sample mean of exposure measurements from their group in evaluating the effect of exposure on the response. Therefore, exposure is estimated on an ecological level while health outcomes are ascertained for each subject. Such study design leads to negligible bias in risk estimates when group means are estimated from 'large' samples. However, in many cases, only a small number of observations are available to estimate the group means, and this causes bias in the observed exposure-disease association. Also, the analysis in a semi-ecological design may involve exposure data with the majority missing and the rest observed with measurement errors and complete response data collected with ascertainment.
In workplaces groups/jobs are naturally ordered and this could be incorporated in estimation procedure by constrained estimation methods together with the expectation and maximization (EM) algorithms for regression models having measurement error and missing values. Four methods were compared by a simulation study: naive complete-case analysis, GBS, the constrained GBS (CGBS), and the constrained expectation and maximization (CEM). We illustrated the methods in the analysis of decline in lung function due to exposures to carbon black.
Naive and GBS approaches were shown to be inadequate when the number of exposure measurements is too small to accurately estimate group means. The CEM method appears to be best among them when within each exposure group at least a 'moderate' number of individuals have their exposures observed with error. However, compared with CEM, CGBS is easier to implement and has more desirable bias-reducing properties in the presence of substantial proportions of missing exposure data.
The CGBS approach could be useful for estimating exposure-disease association in semi-ecological studies when the true group means are ordered and the number of measured exposures in each group is small. These findings have important implication for cost-effective design of semi-ecological studies because they enable investigators to more reliably estimate exposure-disease associations with smaller exposure measurement campaign than with the analytical methods that were historically employed. |
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| AbstractList | In epidemiological studies, it is often not possible to measure accurately exposures of participants even if their response variable can be measured without error. When there are several groups of subjects, occupational epidemiologists employ group-based strategy (GBS) for exposure assessment to reduce bias due to measurement errors: individuals of a group/job within study sample are assigned commonly to the sample mean of exposure measurements from their group in evaluating the effect of exposure on the response. Therefore, exposure is estimated on an ecological level while health outcomes are ascertained for each subject. Such study design leads to negligible bias in risk estimates when group means are estimated from 'large' samples. However, in many cases, only a small number of observations are available to estimate the group means, and this causes bias in the observed exposure-disease association. Also, the analysis in a semi-ecological design may involve exposure data with the majority missing and the rest observed with measurement errors and complete response data collected with ascertainment.
In workplaces groups/jobs are naturally ordered and this could be incorporated in estimation procedure by constrained estimation methods together with the expectation and maximization (EM) algorithms for regression models having measurement error and missing values. Four methods were compared by a simulation study: naive complete-case analysis, GBS, the constrained GBS (CGBS), and the constrained expectation and maximization (CEM). We illustrated the methods in the analysis of decline in lung function due to exposures to carbon black.
Naive and GBS approaches were shown to be inadequate when the number of exposure measurements is too small to accurately estimate group means. The CEM method appears to be best among them when within each exposure group at least a 'moderate' number of individuals have their exposures observed with error. However, compared with CEM, CGBS is easier to implement and has more desirable bias-reducing properties in the presence of substantial proportions of missing exposure data.
The CGBS approach could be useful for estimating exposure-disease association in semi-ecological studies when the true group means are ordered and the number of measured exposures in each group is small. These findings have important implication for cost-effective design of semi-ecological studies because they enable investigators to more reliably estimate exposure-disease associations with smaller exposure measurement campaign than with the analytical methods that were historically employed. BACKGROUND: In epidemiological studies, it is often not possible to measure accurately exposures of participants even if their response variable can be measured without error. When there are several groups of subjects, occupational epidemiologists employ group-based strategy (GBS) for exposure assessment to reduce bias due to measurement errors: individuals of a group/job within study sample are assigned commonly to the sample mean of exposure measurements from their group in evaluating the effect of exposure on the response. Therefore, exposure is estimated on an ecological level while health outcomes are ascertained for each subject. Such study design leads to negligible bias in risk estimates when group means are estimated from 'large' samples. However, in many cases, only a small number of observations are available to estimate the group means, and this causes bias in the observed exposure-disease association. Also, the analysis in a semi-ecological design may involve exposure data with the majority missing and the rest observed with measurement errors and complete response data collected with ascertainment. METHODS: In workplaces groups/jobs are naturally ordered and this could be incorporated in estimation procedure by constrained estimation methods together with the expectation and maximization (EM) algorithms for regression models having measurement error and missing values. Four methods were compared by a simulation study: naive complete-case analysis, GBS, the constrained GBS (CGBS), and the constrained expectation and maximization (CEM). We illustrated the methods in the analysis of decline in lung function due to exposures to carbon black. RESULTS: Naive and GBS approaches were shown to be inadequate when the number of exposure measurements is too small to accurately estimate group means. The CEM method appears to be best among them when within each exposure group at least a 'moderate' number of individuals have their exposures observed with error. However, compared with CEM, CGBS is easier to implement and has more desirable bias-reducing properties in the presence of substantial proportions of missing exposure data. CONCLUSION: The CGBS approach could be useful for estimating exposure-disease association in semi-ecological studies when the true group means are ordered and the number of measured exposures in each group is small. These findings have important implication for cost-effective design of semi-ecological studies because they enable investigators to more reliably estimate exposure-disease associations with smaller exposure measurement campaign than with the analytical methods that were historically employed. Background In epidemiological studies, it is often not possible to measure accurately exposures of participants even if their response variable can be measured without error. When there are several groups of subjects, occupational epidemiologists employ group-based strategy (GBS) for exposure assessment to reduce bias due to measurement errors: individuals of a group/job within study sample are assigned commonly to the sample mean of exposure measurements from their group in evaluating the effect of exposure on the response. Therefore, exposure is estimated on an ecological level while health outcomes are ascertained for each subject. Such study design leads to negligible bias in risk estimates when group means are estimated from 'large' samples. However, in many cases, only a small number of observations are available to estimate the group means, and this causes bias in the observed exposure-disease association. Also, the analysis in a semi-ecological design may involve exposure data with the majority missing and the rest observed with measurement errors and complete response data collected with ascertainment. Methods In workplaces groups/jobs are naturally ordered and this could be incorporated in estimation procedure by constrained estimation methods together with the expectation and maximization (EM) algorithms for regression models having measurement error and missing values. Four methods were compared by a simulation study: naive complete-case analysis, GBS, the constrained GBS (CGBS), and the constrained expectation and maximization (CEM). We illustrated the methods in the analysis of decline in lung function due to exposures to carbon black. Results Naive and GBS approaches were shown to be inadequate when the number of exposure measurements is too small to accurately estimate group means. The CEM method appears to be best among them when within each exposure group at least a 'moderate' number of individuals have their exposures observed with error. However, compared with CEM, CGBS is easier to implement and has more desirable bias-reducing properties in the presence of substantial proportions of missing exposure data. Conclusion The CGBS approach could be useful for estimating exposure-disease association in semi-ecological studies when the true group means are ordered and the number of measured exposures in each group is small. These findings have important implication for cost-effective design of semi-ecological studies because they enable investigators to more reliably estimate exposure-disease associations with smaller exposure measurement campaign than with the analytical methods that were historically employed. Keywords: Constrained estimation, EM algorithm, Group-based strategy, Isotonic regression, Measurement errors In epidemiological studies, it is often not possible to measure accurately exposures of participants even if their response variable can be measured without error. When there are several groups of subjects, occupational epidemiologists employ group-based strategy (GBS) for exposure assessment to reduce bias due to measurement errors: individuals of a group/job within study sample are assigned commonly to the sample mean of exposure measurements from their group in evaluating the effect of exposure on the response. Therefore, exposure is estimated on an ecological level while health outcomes are ascertained for each subject. Such study design leads to negligible bias in risk estimates when group means are estimated from 'large' samples. However, in many cases, only a small number of observations are available to estimate the group means, and this causes bias in the observed exposure-disease association. Also, the analysis in a semi-ecological design may involve exposure data with the majority missing and the rest observed with measurement errors and complete response data collected with ascertainment.BACKGROUNDIn epidemiological studies, it is often not possible to measure accurately exposures of participants even if their response variable can be measured without error. When there are several groups of subjects, occupational epidemiologists employ group-based strategy (GBS) for exposure assessment to reduce bias due to measurement errors: individuals of a group/job within study sample are assigned commonly to the sample mean of exposure measurements from their group in evaluating the effect of exposure on the response. Therefore, exposure is estimated on an ecological level while health outcomes are ascertained for each subject. Such study design leads to negligible bias in risk estimates when group means are estimated from 'large' samples. However, in many cases, only a small number of observations are available to estimate the group means, and this causes bias in the observed exposure-disease association. Also, the analysis in a semi-ecological design may involve exposure data with the majority missing and the rest observed with measurement errors and complete response data collected with ascertainment.In workplaces groups/jobs are naturally ordered and this could be incorporated in estimation procedure by constrained estimation methods together with the expectation and maximization (EM) algorithms for regression models having measurement error and missing values. Four methods were compared by a simulation study: naive complete-case analysis, GBS, the constrained GBS (CGBS), and the constrained expectation and maximization (CEM). We illustrated the methods in the analysis of decline in lung function due to exposures to carbon black.METHODSIn workplaces groups/jobs are naturally ordered and this could be incorporated in estimation procedure by constrained estimation methods together with the expectation and maximization (EM) algorithms for regression models having measurement error and missing values. Four methods were compared by a simulation study: naive complete-case analysis, GBS, the constrained GBS (CGBS), and the constrained expectation and maximization (CEM). We illustrated the methods in the analysis of decline in lung function due to exposures to carbon black.Naive and GBS approaches were shown to be inadequate when the number of exposure measurements is too small to accurately estimate group means. The CEM method appears to be best among them when within each exposure group at least a 'moderate' number of individuals have their exposures observed with error. However, compared with CEM, CGBS is easier to implement and has more desirable bias-reducing properties in the presence of substantial proportions of missing exposure data.RESULTSNaive and GBS approaches were shown to be inadequate when the number of exposure measurements is too small to accurately estimate group means. The CEM method appears to be best among them when within each exposure group at least a 'moderate' number of individuals have their exposures observed with error. However, compared with CEM, CGBS is easier to implement and has more desirable bias-reducing properties in the presence of substantial proportions of missing exposure data.The CGBS approach could be useful for estimating exposure-disease association in semi-ecological studies when the true group means are ordered and the number of measured exposures in each group is small. These findings have important implication for cost-effective design of semi-ecological studies because they enable investigators to more reliably estimate exposure-disease associations with smaller exposure measurement campaign than with the analytical methods that were historically employed.CONCLUSIONThe CGBS approach could be useful for estimating exposure-disease association in semi-ecological studies when the true group means are ordered and the number of measured exposures in each group is small. These findings have important implication for cost-effective design of semi-ecological studies because they enable investigators to more reliably estimate exposure-disease associations with smaller exposure measurement campaign than with the analytical methods that were historically employed. Abstract Background In epidemiological studies, it is often not possible to measure accurately exposures of participants even if their response variable can be measured without error. When there are several groups of subjects, occupational epidemiologists employ group-based strategy (GBS) for exposure assessment to reduce bias due to measurement errors: individuals of a group/job within study sample are assigned commonly to the sample mean of exposure measurements from their group in evaluating the effect of exposure on the response. Therefore, exposure is estimated on an ecological level while health outcomes are ascertained for each subject. Such study design leads to negligible bias in risk estimates when group means are estimated from ‘large’ samples. However, in many cases, only a small number of observations are available to estimate the group means, and this causes bias in the observed exposure-disease association. Also, the analysis in a semi-ecological design may involve exposure data with the majority missing and the rest observed with measurement errors and complete response data collected with ascertainment. Methods In workplaces groups/jobs are naturally ordered and this could be incorporated in estimation procedure by constrained estimation methods together with the expectation and maximization (EM) algorithms for regression models having measurement error and missing values. Four methods were compared by a simulation study: naive complete-case analysis, GBS, the constrained GBS (CGBS), and the constrained expectation and maximization (CEM). We illustrated the methods in the analysis of decline in lung function due to exposures to carbon black. Results Naive and GBS approaches were shown to be inadequate when the number of exposure measurements is too small to accurately estimate group means. The CEM method appears to be best among them when within each exposure group at least a ’moderate’ number of individuals have their exposures observed with error. However, compared with CEM, CGBS is easier to implement and has more desirable bias-reducing properties in the presence of substantial proportions of missing exposure data. Conclusion The CGBS approach could be useful for estimating exposure-disease association in semi-ecological studies when the true group means are ordered and the number of measured exposures in each group is small. These findings have important implication for cost-effective design of semi-ecological studies because they enable investigators to more reliably estimate exposure-disease associations with smaller exposure measurement campaign than with the analytical methods that were historically employed. In epidemiological studies, it is often not possible to measure accurately exposures of participants even if their response variable can be measured without error. When there are several groups of subjects, occupational epidemiologists employ group-based strategy (GBS) for exposure assessment to reduce bias due to measurement errors: individuals of a group/job within study sample are assigned commonly to the sample mean of exposure measurements from their group in evaluating the effect of exposure on the response. Therefore, exposure is estimated on an ecological level while health outcomes are ascertained for each subject. Such study design leads to negligible bias in risk estimates when group means are estimated from 'large' samples. However, in many cases, only a small number of observations are available to estimate the group means, and this causes bias in the observed exposure-disease association. Also, the analysis in a semi-ecological design may involve exposure data with the majority missing and the rest observed with measurement errors and complete response data collected with ascertainment. In workplaces groups/jobs are naturally ordered and this could be incorporated in estimation procedure by constrained estimation methods together with the expectation and maximization (EM) algorithms for regression models having measurement error and missing values. Four methods were compared by a simulation study: naive complete-case analysis, GBS, the constrained GBS (CGBS), and the constrained expectation and maximization (CEM). We illustrated the methods in the analysis of decline in lung function due to exposures to carbon black. Naive and GBS approaches were shown to be inadequate when the number of exposure measurements is too small to accurately estimate group means. The CEM method appears to be best among them when within each exposure group at least a 'moderate' number of individuals have their exposures observed with error. However, compared with CEM, CGBS is easier to implement and has more desirable bias-reducing properties in the presence of substantial proportions of missing exposure data. The CGBS approach could be useful for estimating exposure-disease association in semi-ecological studies when the true group means are ordered and the number of measured exposures in each group is small. These findings have important implication for cost-effective design of semi-ecological studies because they enable investigators to more reliably estimate exposure-disease associations with smaller exposure measurement campaign than with the analytical methods that were historically employed. Doc number: 135 Abstract Background: In epidemiological studies, it is often not possible to measure accurately exposures of participants even if their response variable can be measured without error. When there are several groups of subjects, occupational epidemiologists employ group-based strategy (GBS) for exposure assessment to reduce bias due to measurement errors: individuals of a group/job within study sample are assigned commonly to the sample mean of exposure measurements from their group in evaluating the effect of exposure on the response. Therefore, exposure is estimated on an ecological level while health outcomes are ascertained for each subject. Such study design leads to negligible bias in risk estimates when group means are estimated from 'large' samples. However, in many cases, only a small number of observations are available to estimate the group means, and this causes bias in the observed exposure-disease association. Also, the analysis in a semi-ecological design may involve exposure data with the majority missing and the rest observed with measurement errors and complete response data collected with ascertainment. Methods: In workplaces groups/jobs are naturally ordered and this could be incorporated in estimation procedure by constrained estimation methods together with the expectation and maximization (EM) algorithms for regression models having measurement error and missing values. Four methods were compared by a simulation study: naive complete-case analysis, GBS, the constrained GBS (CGBS), and the constrained expectation and maximization (CEM). We illustrated the methods in the analysis of decline in lung function due to exposures to carbon black. Results: Naive and GBS approaches were shown to be inadequate when the number of exposure measurements is too small to accurately estimate group means. The CEM method appears to be best among them when within each exposure group at least a 'moderate' number of individuals have their exposures observed with error. However, compared with CEM, CGBS is easier to implement and has more desirable bias-reducing properties in the presence of substantial proportions of missing exposure data. Conclusion: The CGBS approach could be useful for estimating exposure-disease association in semi-ecological studies when the true group means are ordered and the number of measured exposures in each group is small. These findings have important implication for cost-effective design of semi-ecological studies because they enable investigators to more reliably estimate exposure-disease associations with smaller exposure measurement campaign than with the analytical methods that were historically employed. |
| ArticleNumber | 135 |
| Audience | Academic |
| Author | Kim, Hyang-Mi Burstyn, Igor Park, Chul Gyu van Tongeren, Martie |
| AuthorAffiliation | 2 School of Mathematics and Statistics, Carleton University, Ottawa, Canada 3 Centre for Human Exposure Science, Institute of Occupational Medicine, Edinburgh, UK 1 Department of Mathematics and Statistics, University of Calgary, Calgary, Canada 4 Department of Environmental and Occupational Health, Drexel University, Philadelphia, USA |
| AuthorAffiliation_xml | – name: 1 Department of Mathematics and Statistics, University of Calgary, Calgary, Canada – name: 2 School of Mathematics and Statistics, Carleton University, Ottawa, Canada – name: 3 Centre for Human Exposure Science, Institute of Occupational Medicine, Edinburgh, UK – name: 4 Department of Environmental and Occupational Health, Drexel University, Philadelphia, USA |
| Author_xml | – sequence: 1 givenname: Hyang-Mi surname: Kim fullname: Kim, Hyang-Mi – sequence: 2 givenname: Chul Gyu surname: Park fullname: Park, Chul Gyu – sequence: 3 givenname: Martie surname: van Tongeren fullname: van Tongeren, Martie – sequence: 4 givenname: Igor surname: Burstyn fullname: Burstyn, Igor |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/22947254$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1093/annhyg/42.2.115 10.1136/oem.55.10.651 10.1111/j.2517-6161.1977.tb01600.x 10.1038/jes.2009.74 10.1093/annhyg/mei041 10.1111/j.1541-0420.2010.01437.x 10.1093/annhyg/40.1.65 10.1111/j.0006-341X.2000.00868.x 10.1214/aos/1176346060 10.1093/biostatistics/kxq083 10.1201/9781420010138 10.1097/EDE.0b013e3182254cc6 10.1111/j.2517-6161.1989.tb01754.x |
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| References | A Szpiro (856_CR16) 2011; 12 T Robertson (856_CR7) 1988 G Yi (856_CR11) 2011; 67 B Armstrong (856_CR1) 1998; 55 E Tielemans (856_CR3) 1998; 42 K Gardiner (856_CR12) 1993; 50 R Carroll (856_CR2) 2006 M Silvapulle (856_CR4) 2005 A Dempster (856_CR8) 1977; 39 C Wu (856_CR9) 1983; 11 H Kim (856_CR5) 2011; 21 856_CR10 I Melijson (856_CR15) 1989; 51 K Gardiner (856_CR13) 1996; 40 M van Tongeren (856_CR14) 2006; 50 M Thoresen (856_CR6) 2000; 56 AJP Szpiro (856_CR17) 2011; 22 9559571 - Ann Occup Hyg. 1998 Feb;42(2):115-9 20528858 - Biometrics. 2011 Mar;67(1):67-75 21716114 - Epidemiology. 2011 Sep;22(5):680-5 21252080 - Biostatistics. 2011 Oct;12(4):610-23 10985228 - Biometrics. 2000 Sep;56(3):868-72 16126761 - Ann Occup Hyg. 2006 Jan;50(1):55-64 20179749 - J Expo Sci Environ Epidemiol. 2011 Mar-Apr;21(2):212-21 9930084 - Occup Environ Med. 1998 Oct;55(10):651-6 8280639 - Br J Ind Med. 1993 Dec;50(12):1082-96 9054303 - Ann Occup Hyg. 1996 Feb;40(1):65-77 |
| References_xml | – volume: 42 start-page: 115 year: 1998 ident: 856_CR3 publication-title: Ann Occup Hyg doi: 10.1093/annhyg/42.2.115 – ident: 856_CR10 – volume: 55 start-page: 651 year: 1998 ident: 856_CR1 publication-title: Occup and Environ Med doi: 10.1136/oem.55.10.651 – volume: 39 start-page: 1 year: 1977 ident: 856_CR8 publication-title: J R Stat Soc, Ser B doi: 10.1111/j.2517-6161.1977.tb01600.x – volume: 21 start-page: 212 year: 2011 ident: 856_CR5 publication-title: J Exposure Sci Environ Epidemiol doi: 10.1038/jes.2009.74 – volume-title: Constrained Statistical Inference year: 2005 ident: 856_CR4 – volume: 50 start-page: 55 year: 2006 ident: 856_CR14 publication-title: Ann Occup Hyg doi: 10.1093/annhyg/mei041 – volume: 67 start-page: 67 year: 2011 ident: 856_CR11 publication-title: Biometrics doi: 10.1111/j.1541-0420.2010.01437.x – volume: 40 start-page: 65 year: 1996 ident: 856_CR13 publication-title: Ann Occup Hyg doi: 10.1093/annhyg/40.1.65 – volume: 56 start-page: 868 year: 2000 ident: 856_CR6 publication-title: Biometrics doi: 10.1111/j.0006-341X.2000.00868.x – volume: 11 start-page: 95 year: 1983 ident: 856_CR9 publication-title: Ann Stat doi: 10.1214/aos/1176346060 – volume: 12 start-page: 610 year: 2011 ident: 856_CR16 publication-title: Biostatistics doi: 10.1093/biostatistics/kxq083 – volume: 50 start-page: 1082 year: 1993 ident: 856_CR12 publication-title: Br J ind Med – volume-title: Order restricted statistical inference year: 1988 ident: 856_CR7 – volume-title: Measurement error in, Nonlinear Models (a modern perspective) year: 2006 ident: 856_CR2 doi: 10.1201/9781420010138 – volume: 22 start-page: 680 year: 2011 ident: 856_CR17 publication-title: Epidemiology doi: 10.1097/EDE.0b013e3182254cc6 – volume: 51 start-page: 127 year: 1989 ident: 856_CR15 publication-title: J R Stat Soc, Ser B doi: 10.1111/j.2517-6161.1989.tb01754.x – reference: 9559571 - Ann Occup Hyg. 1998 Feb;42(2):115-9 – reference: 16126761 - Ann Occup Hyg. 2006 Jan;50(1):55-64 – reference: 9930084 - Occup Environ Med. 1998 Oct;55(10):651-6 – reference: 10985228 - Biometrics. 2000 Sep;56(3):868-72 – reference: 8280639 - Br J Ind Med. 1993 Dec;50(12):1082-96 – reference: 9054303 - Ann Occup Hyg. 1996 Feb;40(1):65-77 – reference: 21716114 - Epidemiology. 2011 Sep;22(5):680-5 – reference: 21252080 - Biostatistics. 2011 Oct;12(4):610-23 – reference: 20179749 - J Expo Sci Environ Epidemiol. 2011 Mar-Apr;21(2):212-21 – reference: 20528858 - Biometrics. 2011 Mar;67(1):67-75 |
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| Snippet | In epidemiological studies, it is often not possible to measure accurately exposures of participants even if their response variable can be measured without... Background In epidemiological studies, it is often not possible to measure accurately exposures of participants even if their response variable can be measured... Doc number: 135 Abstract Background: In epidemiological studies, it is often not possible to measure accurately exposures of participants even if their... Background: In epidemiological studies, it is often not possible to measure accurately exposures of participants even if their response variable can be... BACKGROUND: In epidemiological studies, it is often not possible to measure accurately exposures of participants even if their response variable can be... Abstract Background In epidemiological studies, it is often not possible to measure accurately exposures of participants even if their response variable can be... |
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| SubjectTerms | Algorithms Analysis Bias Bias (Statistics) Black carbon Constrained estimation EM algorithm Epidemiologic Measurements Epidemiologic Studies Epidemiology Estimates Estimation theory Expectation-maximization algorithm Group-based strategy Health aspects Humans Isotonic regression Measurement Measurement errors Medical research Medicine, Experimental Methods Missing observations (Statistics) Occupational Exposure Outcome Assessment (Health Care) Research Design Respiration - drug effects Respiratory Function Tests Risk Assessment Soot - poisoning Statistical methods Studies Sustainable living Variables |
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| Title | Estimation methods with ordered exposure subject to measurement error and missingness in semi-ecological design |
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