scholarly journals A Method to Adjust for Measurement Error in Three Exposures Measured with Correlated Errors in the Absence of Internal Validation Study

2019 ◽  
Author(s):  
Alexander K. Muoka ◽  
George Agogo ◽  
Oscar Ngesa ◽  
Henry Mwambi
Keyword(s):  
Measurement Error ◽  
Validation Study ◽  
Real Data ◽  
Epidemiological Studies ◽  
Correlated Errors ◽  
Exposure Measurement ◽  
Internal Validation ◽  
Exposure Measurement Error ◽  
Correlated Exposures

Abstract Difficulty in obtaining the correct measurement for an individual's long-term exposure is a major challenge in epidemiological studies that investigate the association between exposures and health outcomes. Measurement error in an exposure biases the association between the exposure and a disease outcome. Usually an internal validation study is required to adjust for exposure measurement error; it is challenging if such a study is not available. We proposed a method (trivariate method) that adjusts for measurement error in three correlated exposures in the absence of internal validation study and illustrated the method using real data. We compared the results from the proposed method with those obtained using a method that ignores measurement error and a method that ignores correlations between the errors and true exposures (the univariate method). It was found that ignoring measurement error leads to bias and underestimates the standard error. It was also found that the magnitude of adjustment in the trivariate method is sensitive to the magnitude of measurement error, sign and correlation between the errors. We conclude that the proposed method can be used to adjust for bias in the outcome-exposure association in a case where three exposures are measured with correlated errors in the absence of an internal validation study. The method is useful in conducting a sensitivity analysis on the magnitude of measurement error and the sign of the error correlation.

scholarly journals A Method to adjust for measurement error in multiple exposure variables measured with correlated errors in the absence of an internal validation study

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1486
Author(s):  
Alexander K. Muoka ◽  
George O. Agogo ◽  
Oscar O. Ngesa ◽  
Henry G. Mwambi
Keyword(s):  
Sensitivity Analysis ◽  
Measurement Error ◽  
Validation Study ◽  
Real Data ◽  
Epidemiological Studies ◽  
Correlated Errors ◽  
Internal Validation ◽  
Multivariate Method ◽  
Multiple Exposures ◽  
Naive Method

Difficulty in obtaining the correct measurement for an individual’s longterm exposure is a major challenge in epidemiological studies that investigate the association between exposures and health outcomes. Measurement error in an exposure biases the association between the exposure and a disease outcome. Usually, an internal validation study is required to adjust for exposure measurement error; it is challenging if such a study is not available. We propose a general method for adjusting for measurement error where multiple exposures are measured with correlated errors (a multivariate method) and illustrate the method using real data. We compare the results from the multivariate method with those obtained using a method that ignores measurement error (the naive method) and a method that ignores correlations between the errors and true exposures (the univariate method). It is found that ignoring measurement error leads to bias and underestimates the standard error. A sensitivity analysis shows that the magnitude of adjustment in the multivariate method is sensitive to the magnitude of measurement error, sign, and the correlation between the errors. We conclude that the multivariate method can be used to adjust for bias in the outcome-exposure association in a case where multiple exposures are measured with correlated errors in the absence of an internal validation study. The method is also useful in conducting a sensitivity analysis on the magnitude of measurement error and the sign of the error correlation.

Correcting for the bias caused by exposure measurement error in epidemiological studies

Respirology ◽  
2014 ◽  
Vol 19 (7) ◽  
pp. 979-984 ◽  
Author(s):  
Michael T. Fahey ◽  
Andrew B. Forbes ◽  
Alison M. Hodge
Keyword(s):  
Measurement Error ◽  
Epidemiological Studies ◽  
Exposure Measurement ◽  
Exposure Measurement Error

A Bayesian approach for estimating the partial potential impact fraction with exposure measurement error under a main study/internal validation design

2021 ◽  
pp. 096228022110605
Author(s):  
Xinyuan Chen ◽  
Joseph Chang ◽  
Donna Spiegelman ◽  
Fan Li
Keyword(s):  
Measurement Error ◽  
Health Professionals ◽  
Main Study ◽  
Exposure Measurement ◽  
Internal Validation ◽  
Follow Up Study ◽  
Exposure Measurement Error ◽  
Potential Impact ◽  
Potential Impact Fraction

The partial potential impact fraction describes the proportion of disease cases that can be prevented if the distribution of modifiable continuous exposures is shifted in a population, while other risk factors are not modified. It is a useful quantity for evaluating the burden of disease in epidemiologic and public health studies. When exposures are measured with error, the partial potential impact fraction estimates may be biased, which necessitates methods to correct for the exposure measurement error. Motivated by the health professionals follow-up study, we develop a Bayesian approach to adjust for exposure measurement error when estimating the partial potential impact fraction under the main study/internal validation study design. We adopt the reclassification approach that leverages the strength of the main study/internal validation study design and clarifies transportability assumptions for valid inference. We assess the finite-sample performance of both the point and credible interval estimators via extensive simulations and apply the proposed approach in the health professionals follow-up study to estimate the partial potential impact fraction for colorectal cancer incidence under interventions exploring shifting the distributions of red meat, alcohol, and/or folate intake.

scholarly journals The Impact of Individual Mobility on Long-Term Exposure to Ambient PM2.5: Assessing Effect Modification by Travel Patterns and Spatial Variability of PM2.5

2021 ◽  
Vol 18 (4) ◽  
pp. 2194
Author(s):  
Eun-hye Yoo ◽  
Qiang Pu ◽  
Youngseob Eum ◽  
Xiangyu Jiang
Keyword(s):  
Air Pollution ◽  
Measurement Error ◽  
Effect Modification ◽  
Spatiotemporal Variability ◽  
Exposure Model ◽  
Travel Patterns ◽  
Exposure Measurement ◽  
Exposure Measurement Error ◽  
The Impact

The impact of individuals’ mobility on the degree of error in estimates of exposure to ambient PM2.5 concentrations is increasingly reported in the literature. However, the degree to which accounting for mobility reduces error likely varies as a function of two related factors—individuals’ routine travel patterns and the local variations of air pollution fields. We investigated whether individuals’ routine travel patterns moderate the impact of mobility on individual long-term exposure assessment. Here, we have used real-world time–activity data collected from 2013 participants in Erie/Niagara counties, New York, USA, matched with daily PM2.5 predictions obtained from two spatial exposure models. We further examined the role of the spatiotemporal representation of ambient PM2.5 as a second moderator in the relationship between an individual’s mobility and the exposure measurement error using a random effect model. We found that the effect of mobility on the long-term exposure estimates was significant, but that this effect was modified by individuals’ routine travel patterns. Further, this effect modification was pronounced when the local variations of ambient PM2.5 concentrations were captured from multiple sources of air pollution data (‘a multi-sourced exposure model’). In contrast, the mobility effect and its modification were not detected when ambient PM2.5 concentration was estimated solely from sparse monitoring data (‘a single-sourced exposure model’). This study showed that there was a significant association between individuals’ mobility and the long-term exposure measurement error. However, the effect could be modified by individuals’ routine travel patterns and the error-prone representation of spatiotemporal variability of PM2.5.

scholarly journals Quantifying the short-term effects of air pollution on health in the presence of exposure measurement error: a simulation study of multi-pollutant model results

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Dimitris Evangelopoulos ◽  
Klea Katsouyanni ◽  
Joel Schwartz ◽  
Heather Walton
Keyword(s):  
Air Pollution ◽  
Measurement Error ◽  
Simulation Study ◽  
Epidemiological Studies ◽  
Effect Estimate ◽  
Exposure Measurement ◽  
Exposure Models ◽  
Mortality Effect ◽  
Exposure Measurement Error ◽  
The Impact

Abstract Background Most epidemiological studies estimate associations without considering exposure measurement error. While some studies have estimated the impact of error in single-exposure models we aimed to quantify the effect of measurement error in multi-exposure models, specifically in time-series analysis of PM2.5, NO2, and mortality using simulations, under various plausible scenarios for exposure errors. Measurement error in multi-exposure models can lead to effect transfer where the effect estimate is overestimated for the pollutant estimated with more error to the one estimated with less error. This complicates interpretation of the independent effects of different pollutants and thus the relative importance of reducing their concentrations in air pollution policy. Methods Measurement error was defined as the difference between ambient concentrations and personal exposure from outdoor sources. Simulation inputs for error magnitude and variability were informed by the literature. Error-free exposures with their consequent health outcome and error-prone exposures of various error types (classical/Berkson) were generated. Bias was quantified as the relative difference in effect estimates of the error-free and error-prone exposures. Results Mortality effect estimates were generally underestimated with greater bias observed when low ratios of the true exposure variance over the error variance were assumed (27.4% underestimation for NO2). Higher ratios resulted in smaller, but still substantial bias (up to 19% for both pollutants). Effect transfer was observed indicating that less precise measurements for one pollutant (NO2) yield more bias, while the co-pollutant (PM2.5) associations were found closer to the true. Interestingly, the sum of single-pollutant model effect estimates was found closer to the summed true associations than those from multi-pollutant models, due to cancelling out of confounding and measurement error bias. Conclusions Our simulation study indicated an underestimation of true independent health effects of multiple exposures due to measurement error. Using error parameter information in future epidemiological studies should provide more accurate concentration-response functions.

scholarly journals The Effect of Random Error in Exposure Measurement upon the Shape of the Exposure Response

Dose-Response ◽  
2005 ◽  
Vol 3 (4) ◽  
pp. dose-response.0 ◽  
Author(s):  
Kenny S. Crump
Keyword(s):  
Measurement Error ◽  
Measurement Errors ◽  
Epidemiological Data ◽  
Epidemiological Studies ◽  
Random Errors ◽  
Exposure Measurement ◽  
Considerable Uncertainty ◽  
Exposure Response ◽  
Log Normal ◽  
Normally Distributed

Although statistical analyses of epidemiological data usually treat the exposure variable as being known without error, estimated exposures in epidemiological studies often involve considerable uncertainty. This paper investigates the theoretical effect of random errors in exposure measurement upon the observed shape of the exposure response. The model utilized assumes that true exposures are log-normally distributed, and multiplicative measurement errors are also log-normally distributed and independent of the true exposures. Under these conditions it is shown that whenever the true exposure response is proportional to exposure to a power r, the observed exposure response is proportional to exposure to a power K, where K < r. This implies that the observed exposure response exaggerates risk, and by arbitrarily large amounts, at sufficiently small exposures. It also follows that a truly linear exposure response will appear to be supra-linear—i.e., a linear function of exposure raised to the K-th power, where K is less than 1.0. These conclusions hold generally under the stated log-normal assumptions whenever there is any amount of measurement error, including, in particular, when the measurement error is unbiased either in the natural or log scales. Equations are provided that express the observed exposure response in terms of the parameters of the underlying log-normal distribution. A limited investigation suggests that these conclusions do not depend upon the log-normal assumptions, but hold more widely. Because of this problem, in addition to other problems in exposure measurement, shapes of exposure responses derived empirically from epidemiological data should be treated very cautiously. In particular, one should be cautious in concluding that the true exposure response is supra-linear on the basis of an observed supra-linear form.

scholarly journals Home Endotoxin Exposure and Wheeze in Infants: Correction for Bias Due to Exposure Measurement Error

2006 ◽  
Vol 114 (1) ◽  
pp. 135-140 ◽  
Author(s):  
Nora Horick ◽  
Edie Weller ◽  
Donald K. Milton ◽  
Diane R. Gold ◽  
Ruifeng Li ◽  
...  
Keyword(s):  
Measurement Error ◽  
Exposure Measurement ◽  
Endotoxin Exposure ◽  
Exposure Measurement Error
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