Adjusting for Berkson error in exposure in ordinary and conditional logistic regression and in Poisson regression
Abstract Background INTEROCC is a seven-country cohort study of occupational exposures and brain cancer risk, including occupational exposure to electromagnetic fields (EMF). In the absence of data on individual exposures, a Job Exposure Matrix (JEM) may be used to construct likely exposure scenario...
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| Format: | Article |
| Language: | English |
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BMC
2023-10-01
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| Series: | BMC Medical Research Methodology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12874-023-02044-x |
| _version_ | 1797558859606261760 |
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| author | Tamer Oraby Santanu Chakraborty Siva Sivaganesan Laurel Kincl Lesley Richardson Mary McBride Jack Siemiatycki Elisabeth Cardis Daniel Krewski |
| author_facet | Tamer Oraby Santanu Chakraborty Siva Sivaganesan Laurel Kincl Lesley Richardson Mary McBride Jack Siemiatycki Elisabeth Cardis Daniel Krewski |
| author_sort | Tamer Oraby |
| collection | DOAJ |
| description | Abstract Background INTEROCC is a seven-country cohort study of occupational exposures and brain cancer risk, including occupational exposure to electromagnetic fields (EMF). In the absence of data on individual exposures, a Job Exposure Matrix (JEM) may be used to construct likely exposure scenarios in occupational settings. This tool was constructed using statistical summaries of exposure to EMF for various occupational categories for a comparable group of workers. Methods In this study, we use the Canadian data from INTEROCC to determine the best EMF exposure surrogate/estimate from three appropriately chosen surrogates from the JEM, along with a fourth surrogate based on Berkson error adjustments obtained via numerical approximation of the likelihood function. In this article, we examine the case in which exposures are gamma-distributed for each occupation in the JEM, as an alternative to the log-normal exposure distribution considered in a previous study conducted by our research team. We also study using those surrogates and the Berkson error adjustment in Poisson regression and conditional logistic regression. Results Simulations show that the introduced methods of Berkson error adjustment for non-stratified analyses provide accurate estimates of the risk of developing tumors in case of gamma exposure model. Alternatively, and under some technical assumptions, the arithmetic mean is the best surrogate when a gamma-distribution is used as an exposure model. Simulations also show that none of the present methods could provide an accurate estimate of the risk in case of stratified analyses. Conclusion While our previous study found the geometric mean to be the best exposure surrogate, the present study suggests that the best surrogate is dependent on the exposure model; the arithmetic means in case of gamma-exposure model and the geometric means in case of log-normal exposure model. However, we could present a better method of Berkson error adjustment for each of the two exposure models. Our results provide useful guidance on the application of JEMs for occupational exposure assessments, with adjustment for Berkson error. |
| first_indexed | 2024-03-10T17:36:21Z |
| format | Article |
| id | doaj.art-38307361c26b4189936da95190b42366 |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-03-10T17:36:21Z |
| publishDate | 2023-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-38307361c26b4189936da95190b423662023-11-20T09:49:29ZengBMCBMC Medical Research Methodology1471-22882023-10-0123111010.1186/s12874-023-02044-xAdjusting for Berkson error in exposure in ordinary and conditional logistic regression and in Poisson regressionTamer Oraby0Santanu Chakraborty1Siva Sivaganesan2Laurel Kincl3Lesley Richardson4Mary McBride5Jack Siemiatycki6Elisabeth Cardis7Daniel Krewski8School of Mathematical and Statistical Sciences, University of Texas Rio Grande ValleySchool of Mathematical and Statistical Sciences, University of Texas Rio Grande ValleyDepartment of Mathematical Sciences, University of CincinnatiCollege of Health, Oregon State UniversityCRCHUM, Centre de Recherche Hospitalier de L’université de MontréalBC Cancer AgencyCRCHUM, Centre de Recherche Hospitalier de L’université de MontréalBarcelona Institute for Global Health (ISGlobal)McLaughlin Centre for Population Health Risk Assessment, University of OttawaAbstract Background INTEROCC is a seven-country cohort study of occupational exposures and brain cancer risk, including occupational exposure to electromagnetic fields (EMF). In the absence of data on individual exposures, a Job Exposure Matrix (JEM) may be used to construct likely exposure scenarios in occupational settings. This tool was constructed using statistical summaries of exposure to EMF for various occupational categories for a comparable group of workers. Methods In this study, we use the Canadian data from INTEROCC to determine the best EMF exposure surrogate/estimate from three appropriately chosen surrogates from the JEM, along with a fourth surrogate based on Berkson error adjustments obtained via numerical approximation of the likelihood function. In this article, we examine the case in which exposures are gamma-distributed for each occupation in the JEM, as an alternative to the log-normal exposure distribution considered in a previous study conducted by our research team. We also study using those surrogates and the Berkson error adjustment in Poisson regression and conditional logistic regression. Results Simulations show that the introduced methods of Berkson error adjustment for non-stratified analyses provide accurate estimates of the risk of developing tumors in case of gamma exposure model. Alternatively, and under some technical assumptions, the arithmetic mean is the best surrogate when a gamma-distribution is used as an exposure model. Simulations also show that none of the present methods could provide an accurate estimate of the risk in case of stratified analyses. Conclusion While our previous study found the geometric mean to be the best exposure surrogate, the present study suggests that the best surrogate is dependent on the exposure model; the arithmetic means in case of gamma-exposure model and the geometric means in case of log-normal exposure model. However, we could present a better method of Berkson error adjustment for each of the two exposure models. Our results provide useful guidance on the application of JEMs for occupational exposure assessments, with adjustment for Berkson error.https://doi.org/10.1186/s12874-023-02044-xBerkson errorExposure surrogateElectromagnetic fieldsBrain cancerConditional logistic regressionPoisson regression |
| spellingShingle | Tamer Oraby Santanu Chakraborty Siva Sivaganesan Laurel Kincl Lesley Richardson Mary McBride Jack Siemiatycki Elisabeth Cardis Daniel Krewski Adjusting for Berkson error in exposure in ordinary and conditional logistic regression and in Poisson regression BMC Medical Research Methodology Berkson error Exposure surrogate Electromagnetic fields Brain cancer Conditional logistic regression Poisson regression |
| title | Adjusting for Berkson error in exposure in ordinary and conditional logistic regression and in Poisson regression |
| title_full | Adjusting for Berkson error in exposure in ordinary and conditional logistic regression and in Poisson regression |
| title_fullStr | Adjusting for Berkson error in exposure in ordinary and conditional logistic regression and in Poisson regression |
| title_full_unstemmed | Adjusting for Berkson error in exposure in ordinary and conditional logistic regression and in Poisson regression |
| title_short | Adjusting for Berkson error in exposure in ordinary and conditional logistic regression and in Poisson regression |
| title_sort | adjusting for berkson error in exposure in ordinary and conditional logistic regression and in poisson regression |
| topic | Berkson error Exposure surrogate Electromagnetic fields Brain cancer Conditional logistic regression Poisson regression |
| url | https://doi.org/10.1186/s12874-023-02044-x |
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