OBJECTIVES: Individual assessment of exposure by experts and the use of a job-exposure matrix are the two main methods of evaluating past occupational exposures in community-based case-referent studies. The objective of this study was to compare the performance of the estimations of the odds ratio in the two methods. This paper focuses on job-exposure matrices whose entries consist of proportions of persons exposed. METHODS: Simulations were used to compare the variances of the estimations of the odds ratios obtained with the two methods and to study the consequences with respect to bias and the precision of the odds ratios estimated for misclassifications of exposure produced by either the experts or the matrix. RESULTS: When there was no misclassification, the results showed that the precision obtained with the job-exposure matrix was about three times less than that achieved by experts in a large range of practical situations. However, when potential errors of exposure assessment were taken into account, the simulations suggested that the test of the hypothesis OR = 1 against the alternative OR not equal to 1 when exposure was assessed with an unbiased job-exposure matrix had a statistical power close to that obtained when exposure was assessed by an expert with high sensibility and specificity. CONCLUSIONS: The evaluation of exposure with an unbiased job-exposure matrix in studies of the association between exposure and disease had a statistical power close to that expected in practice with a good expert in the large range of practical situations which were investigated.
OBJECTIVES: Individual assessment of exposure by experts and the use of a job-exposure matrix are the two main methods of evaluating past occupational exposures in community-based case-referent studies. The objective of this study was to compare the performance of the estimations of the odds ratio in the two methods. This paper focuses on job-exposure matrices whose entries consist of proportions of persons exposed. METHODS: Simulations were used to compare the variances of the estimations of the odds ratios obtained with the two methods and to study the consequences with respect to bias and the precision of the odds ratios estimated for misclassifications of exposure produced by either the experts or the matrix. RESULTS: When there was no misclassification, the results showed that the precision obtained with the job-exposure matrix was about three times less than that achieved by experts in a large range of practical situations. However, when potential errors of exposure assessment were taken into account, the simulations suggested that the test of the hypothesis OR = 1 against the alternative OR not equal to 1 when exposure was assessed with an unbiased job-exposure matrix had a statistical power close to that obtained when exposure was assessed by an expert with high sensibility and specificity. CONCLUSIONS: The evaluation of exposure with an unbiased job-exposure matrix in studies of the association between exposure and disease had a statistical power close to that expected in practice with a good expert in the large range of practical situations which were investigated.
Authors: J Björk; M Albin; H Welinder; H Tinnerberg; N Mauritzson; T Kauppinen; U Strömberg; B Johansson; R Billström; Z Mikoczy; T Ahlgren; P G Nilsson; F Mitelman; L Hagmar Journal: Occup Environ Med Date: 2001-11 Impact factor: 4.402
Authors: C Quinot; O Dumas; P K Henneberger; R Varraso; A S Wiley; F E Speizer; M Goldberg; J P Zock; C A Camargo; N Le Moual Journal: Occup Environ Med Date: 2016-08-26 Impact factor: 4.402
Authors: Natalie McGlynn; Victoria A Kirsh; Michelle Cotterchio; M Anne Harris; Victoria Nadalin; Nancy Kreiger Journal: PLoS One Date: 2015-09-16 Impact factor: 3.240
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Authors: Aude Lacourt; Elisabeth Cardis; Javier Pintos; Lesley Richardson; Laurel Kincl; Geza Benke; Sarah Fleming; Martine Hours; Daniel Krewski; Dave McLean; Marie-Elise Parent; Siegal Sadetzki; Klaus Schlaefer; Brigitte Schlehofer; Jerome Lavoue; Martie van Tongeren; Jack Siemiatycki Journal: BMC Public Health Date: 2013-04-12 Impact factor: 3.295