The Effects of Misclassification on the Estimation of Relative Risk

Barron, Bruce

doi:10.2307/2529795

Cited by 154 publications

(100 citation statements)

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“…Since the occupational information available in this study reflected only usual (longest held) employment, it is possible that effects from short-term occupational pursuits were missed. If misclassification bias was present, it would tend to underestimate the true risk.34 35 And finally, since information for this study was drawn from a number of hospitals, the possibility of differential reporting existed. However, there is no reason to believe that exposure data were selectively biased for cases or controls.…”

Section: Discussionmentioning

confidence: 99%

Occupation, smoking, and alcohol in the epidemiology of bladder cancer.

Brownson¹,

Chang²,

Davis³

1987

Am J Public Health

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Section: Discussionmentioning

confidence: 99%

Occupation, smoking, and alcohol in the epidemiology of bladder cancer.

Brownson¹,

Chang²,

Davis³

1987

Am J Public Health

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“…Estimation of ppv y and npv y is discussed below. An alternative approach would be to formulate the misclassification using sensitivities and specificities (Barron, 1977). It can be shown that the probabilities η X xy are estimated usingη X 1y = ppv yη…”

Section: Methods Using Estimated Misclassification Probabilitiesmentioning

confidence: 99%

“…Perhaps the most intuitive approach is to employ methods for correcting for misclassification of fundamentally categorical exposures, that is categorical exposures which are not derived from an underlying continuous measure. These methods are based on estimated misclassification probabilities (Barron, 1977, Morrissey and Spiegelman, 1999, Kosinski and Flanders, 1999, Chu et al, 2008. A disadvantage of these methods is that they are focused on estimation of odds ratios, and therefore not applicable in a more general context.…”

Section: Introductionmentioning

confidence: 99%

Correcting for Bias due to Misclassification when Error-prone Continuous Exposures Are Misclassified

Keogh¹,

Strawbridge²,

White³

2012

Epidemiologic Methods

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To investigate the association between a continuous exposure and an outcome it is common to categorize the exposure and estimate the relative associations between categories. Error in measurement of the continuous exposure results in misclassification when the exposure is categorized. In this paper we investigate methods for correcting for this misclassification. We consider applications of methods for continuous exposures and for fundamentally categorical exposures. A particular challenge is that even nondifferential error in the underlying continuous exposure can result in differential misclassification in the categorized exposure, i.e. misclassification dependent on the outcome. For continuous exposures, there exist a range of methods for correcting for the effects of exposure measurement error on the exposure-outcome association, including regression calibration (RC), multiple imputation (MI), moment reconstruction (MR) and simulation extrapolation (SIMEX). There are also correction methods for use with genuinely categorical exposures, using estimated misclassification probabilities. Alongside simple methods using estimated misclassification probabilities, we also consider two RC based methods, MI and MR of the continuous exposure followed by categorization, and a new SIMEX method. Simulation studies are used to compare the methods when the true exposure is available in a validation study and the more common situation in which replicate or additional error-prone exposure measurements are available in a subsample. We restrict attention to the case where the underlying association between the continuous exposure and the outcome is linear on the appropriate scale. RC and SIMEX methods fail to correct adequately for bias. However, MI and MR perform well. Methods using estimated misclassification probabilities also perform well, provided differential misclassification is assumed, however these methods are restricted to estimation of odds ratios and have other practical drawbacks. MI and MR have the benefit of being flexible for use with different analysis models, with quantile-based cutpoints, and more easily accommodate covariate adjustment. In summary, we found that MI and MR can be applied to correct exposureoutcome associations for the effects of misclassification error when the association is linear. Extending MI and MR for use with categorized continuous exposures under nonlinear exposureoutcome associations is now an important area for further research.

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“…The identity (6) allows us to estimate the probability p ijk using the estimates of p * ijk which can be obtained from the observed counts (Barron 1977). Let n * ijk represent the number of cases or controls with the observed measurement X * a = j, X * s = k for i, j, k = 0, 1, as displayed in Table 1.…”

Section: Yi and He Journal Of Statistical Distributions And Applicatimentioning

confidence: 99%

Analysis of case-control data with interacting misclassified covariates

2017

J Stat Distrib App

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Case-control studies are important and useful methods for studying health outcomes and many methods have been developed for analyzing case-control data. Those methods, however, are vulnerable to mismeasurement of variables; biased results are often produced if such a feature is ignored. In this paper, we develop an inference method for handling case-control data with interacting misclassified covariates. We use the prospective logistic regression model to feature the development of the disease. To characterize the misclassification process, we consider a practical situation where replicated measurements of error-prone covariates are available. Our work is motivated in part by a breast cancer case-control study where two binary covariates are subject to misclassification. Extensions to other settings are outlined.

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The Effects of Misclassification on the Estimation of Relative Risk

Cited by 154 publications

References 0 publications

Occupation, smoking, and alcohol in the epidemiology of bladder cancer.

Occupation, smoking, and alcohol in the epidemiology of bladder cancer.

Correcting for Bias due to Misclassification when Error-prone Continuous Exposures Are Misclassified

Analysis of case-control data with interacting misclassified covariates

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