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Association between covariates and disease occurrence in the presence of diagnostic error


Lewis, F; Sanchez-Vazquez, M J; Torgerson, P R (2012). Association between covariates and disease occurrence in the presence of diagnostic error. Epidemiology and Infection, 140(08):1515-1524.

Abstract

Identification of covariates associated with disease is a key part of epidemiological research. Yet, while adjustment for imperfect diagnostic accuracy is well established when estimating disease prevalence, similar adjustment when estimating covariate effects is far less common, although of important practical relevance due to the sensitivity of such analyses to misclassification error. Case-study data exploring evidence for seasonal differences in Salmonella prevalence using serological testing is presented, in addition simulated data with known properties are analysed. It is demonstrated that: (i) adjusting for misclassification error in models comprising continuous covariates can have a very substantial impact on the resulting conclusions which can then be drawn from any analyses; and (ii) incorporating prior knowledge through Bayesian estimation can provide potentially more informative assessments of covariates while removing the assumption of perfect diagnostic accuracy. The method presented is widely applicable and easily generalized to many types of epidemiological studies.

Abstract

Identification of covariates associated with disease is a key part of epidemiological research. Yet, while adjustment for imperfect diagnostic accuracy is well established when estimating disease prevalence, similar adjustment when estimating covariate effects is far less common, although of important practical relevance due to the sensitivity of such analyses to misclassification error. Case-study data exploring evidence for seasonal differences in Salmonella prevalence using serological testing is presented, in addition simulated data with known properties are analysed. It is demonstrated that: (i) adjusting for misclassification error in models comprising continuous covariates can have a very substantial impact on the resulting conclusions which can then be drawn from any analyses; and (ii) incorporating prior knowledge through Bayesian estimation can provide potentially more informative assessments of covariates while removing the assumption of perfect diagnostic accuracy. The method presented is widely applicable and easily generalized to many types of epidemiological studies.

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4 citations in Web of Science®
2 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:05 Vetsuisse Faculty > Chair in Veterinary Epidemiology
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Language:English
Date:2012
Deposited On:06 Mar 2012 12:17
Last Modified:05 Apr 2016 15:32
Publisher:Cambridge University Press
ISSN:0950-2688
Publisher DOI:https://doi.org/10.1017/S0950268811001932

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