Adjusting for Berkson error in exposure in ordinary and conditional logistic regression and in Poisson regression

BMC Medical Research Methodology

Research Article

Mary McBride¹  Elisabeth Cardis²  Lesley Richardson³  Jack Siemiatycki³  Laurel Kincl⁴  Siva Sivaganesan⁵  Daniel Krewski⁶  Santanu Chakraborty⁷  Tamer Oraby⁷ 

[1] BC Cancer Agency, Vancouver, BC, Canada;Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain;Pompeu Fabra University, Barcelona, Spain;Spanish Consortium for Research and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain;CRCHUM, Centre de Recherche Hospitalier de L’université de Montréal, Montreal, QC, Canada;College of Health, Oregon State University, Corvallis, OR, USA;Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH, USA;McLaughlin Centre for Population Health Risk Assessment, University of Ottawa, Ottawa, ON, Canada;Department of Epidemiology and Community Medicine, Faculty of Medicine, University of Ottawa, Ottawa, Canada;Risk Sciences International, Ottawa, Canada;School of Mathematical and Statistical Sciences, University of Texas Rio Grande Valley, Edinburg, TX, USA;

关键词: Berkson error;    Exposure surrogate;    Electromagnetic fields;    Brain cancer;    Conditional logistic regression;    Poisson regression;   

DOI  :  10.1186/s12874-023-02044-x

 received in 2022-08-16, accepted in 2023-09-26,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundINTEROCC 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.MethodsIn 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.ResultsSimulations 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.ConclusionWhile 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.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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