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Lipid adjustment for chemical exposures: accounting for concomitant variables.

ABSTRACT: Some environmental chemical exposures are lipophilic and need to be adjusted by serum lipid levels before data analyses. There are currently various strategies that attempt to account for this problem, but all have their drawbacks. To address such concerns, we propose a new method that uses Box-Cox transformations and a simple Bayesian hierarchical model to adjust for lipophilic chemical exposures.We compared our Box-Cox method to existing methods. We ran simulation studies in which increasing levels of lipid-adjusted chemical exposure did and did not increase the odds of having a disease, and we looked at both single-exposure and multiple-exposure cases. We also analyzed an epidemiology dataset that examined the effects of various chemical exposure on the risk of birth defects.Compared with existing methods, our Box-Cox method produced unbiased estimates, good coverage, similar power, and lower type I error rates. This was the case in both single- and multiple-exposure simulation studies. Results from analysis of the birth-defect data differed from results using existing methods.Our Box-Cox method is a novel and intuitive way to account for the lipophilic nature of certain chemical exposures. It addresses some of the problems with existing methods, is easily extendable to multiple exposures, and can be used in any analysis that involves concomitant variables.

SUBMITTER: Li D

PROVIDER: S-EPMC3812826 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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Lipid adjustment for chemical exposures: accounting for concomitant variables.

Li Daniel D Longnecker Matthew P MP Dunson David B DB

Epidemiology (Cambridge, Mass.) 20131101 6

<h4>Background</h4>Some environmental chemical exposures are lipophilic and need to be adjusted by serum lipid levels before data analyses. There are currently various strategies that attempt to account for this problem, but all have their drawbacks. To address such concerns, we propose a new method that uses Box-Cox transformations and a simple Bayesian hierarchical model to adjust for lipophilic chemical exposures.<h4>Methods</h4>We compared our Box-Cox method to existing methods. We ran simul ...[more]

PMID: 24051893

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Project description:In epidemiological studies explanatory variables are frequently subject to measurement error. The aim of this paper is to develop a Bayesian method to correct for measurement error in multiple continuous exposures in individually matched case-control studies. This is a topic that has not been widely investigated. The new method is illustrated using data from an individually matched case-control study of the association between thyroid hormone levels during pregnancy and exposure to perfluorinated acids. The objective of the motivating study was to examine the risk of maternal hypothyroxinemia due to exposure to three perfluorinated acids measured on a continuous scale. Results from the proposed method are compared with those obtained from a naive analysis.Using a Bayesian approach, the developed method considers a classical measurement error model for the exposures, as well as the conditional logistic regression likelihood as the disease model, together with a random-effect exposure model. Proper and diffuse prior distributions are assigned, and results from a quality control experiment are used to estimate the perfluorinated acids' measurement error variability. As a result, posterior distributions and 95% credible intervals of the odds ratios are computed. A sensitivity analysis of method's performance in this particular application with different measurement error variability was performed.The proposed Bayesian method to correct for measurement error is feasible and can be implemented using statistical software. For the study on perfluorinated acids, a comparison of the inferences which are corrected for measurement error to those which ignore it indicates that little adjustment is manifested for the level of measurement error actually exhibited in the exposures. Nevertheless, a sensitivity analysis shows that more substantial adjustments arise if larger measurement errors are assumed.In individually matched case-control studies, the use of conditional logistic regression likelihood as a disease model in the presence of measurement error in multiple continuous exposures can be justified by having a random-effect exposure model. The proposed method can be successfully implemented in WinBUGS to correct individually matched case-control studies for several mismeasured continuous exposures under a classical measurement error model.

Project description:BackgroundThe impact of nitrogen dioxide (NO2) and particulate matter with an aerodynamic diameter of less than or equal to 2.5. microns (PM2.5) exposures on lung function has been investigated mainly in children and less in adults. Furthermore, it is unclear whether short-term deviations of air pollutant concentration need to be considered in long-term exposure models.ObjectivesThe aims of this study were to investigate the association between short-term air pollution exposure and lung function and to assess whether short-term deviations of air pollutant concentration should be integrated into long-term exposure models.MethodsShort-term (daily averages 0-7 d prior) and long-term (1- and 4-y means) NO2 and PM2.5 concentrations were modeled using satellite, land use, and meteorological data calibrated on ground measurements. Forced expiratory volume within the first second (FEV1) of forced exhalation and forced vital capacity (FVC) were measured during a LuftiBus assessment (2003-2012) and linked to exposure information from the Swiss National Cohort for 36,085 adults (ages 18-95 y). We used multiple linear regression to estimate adjusted associations, and additionally adjusted models of long-term exposures for short-term deviations in air pollutant concentrations.ResultsA 10μg/m3 increase in NO2 and PM2.5 on the day of the pulmonary function test was associated with lower FEV1 and FVC (NO2: FEV1 -8.0 ml [95% confidence interval: -13.4, -2.7], FVC -16.7 ml [-23.4, -10.0]; PM2.5: FEV1 -15.3 ml [-21.9, -8.7], FVC -18.5 ml [-26.5, -10.5]). A 10μg/m3 increase in 1-y mean NO2 was also associated with lower FEV1 (-7.7 ml; -15.9, 0.5) and FVC (-21.6 ml; -31.9, -11.4), as was a 10μg/m3 increase in 1-y mean PM2.5 (FEV1: -42.2 ml; -56.9, -27.5; FVC: -82.0 ml; -100.1, -63.9). These associations were robust to adjustment for short-term deviations in the concentration of each air pollutant.ConclusionsShort- and long-term air pollution exposures were negatively associated with lung function, in particular long-term PM2.5 exposure with FVC. Our findings contribute substantially to the evidence of adverse associations between air pollution and lung function in adults. https://doi.org/10.1289/EHP7529.

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Lipid adjustment for chemical exposures: accounting for concomitant variables.

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Lipid adjustment for chemical exposures: accounting for concomitant variables.

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