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Effects of Disease Misclassification on Exposure–Disease Association

Bibliographic Data

ID11022051
AuthorsQixuan Chen (0000-0002-4199-1766, Qixuan Chen is with the Department of Biostatistics, Hanga Galfalvy is with the Division of Molecular Imaging and Neuropathology, and Naihua Duan is with the Department of Biostatistics and the Division of Biostatistics and Data Coordination, Department of Psychiatry, Columbia University, New York, NY. Hanga Galfalvy and Naihua Duan are also with the New York State Psychiatric Institute, New York.), Hanga Galfalvy (0000-0001-7156-6106, Qixuan Chen is with the Department of Biostatistics, Hanga Galfalvy is with the Division of Molecular Imaging and Neuropathology, and Naihua Duan is with the Department of Biostatistics and the Division of Biostatistics and Data Coordination, Department of Psychiatry, Columbia University, New York, NY. Hanga Galfalvy and Naihua Duan are also with the New York State Psychiatric Institute, New York.), Naihua Duan (0000-0001-9411-2924, Qixuan Chen is with the Department of Biostatistics, Hanga Galfalvy is with the Division of Molecular Imaging and Neuropathology, and Naihua Duan is with the Department of Biostatistics and the Division of Biostatistics and Data Coordination, Department of Psychiatry, Columbia University, New York, NY. Hanga Galfalvy and Naihua Duan are also with the New York State Psychiatric Institute, New York.)
Year2013
Volume103
Issue5
Pagese67-e73
Publication date2013-05-01
Open AccessNo
TypeARTICLE
VenueAmerican Journal of Public Health (JOURNAL)
Journal identifiersISSN: 0090-0036 • E-ISSN: 1541-0048
PublisherAmerican Public Health Association (PUBLISHER • US)
DOI10.2105/ajph.2012.300995
PMID23488509
PMCIDPMC3698812
OpenAlexW1984450960
LanguageEN
Citations received2
References cited11

Objectives. We explore how misclassification in disease status can distort the exposure–disease association in a study with dichotomous disease and exposure status. Methods. We define the difference in population odds ratios between populations with and without disease misclassification as population-level bias and derive the bias as a function of sensitivity and specificity for observed disease status. The magnitude and direction of bias can be elucidated through analytic derivations, as illustrated with numerical examples. Results. Patterns of bias exist not only for nondifferential misclassification but also for some differential misclassification scenarios. We have provided conditions defined in terms of sensitivity and specificity that correspond to each pattern of bias. Conclusions. Caution is needed in interpreting results when misclassification is present. Our findings can be used to assess the effects of disease misclassification in a population when sensitivity and specificity are known or can be estimated

Association (psychology) · Disease · Environmental health · Information bias · Logistic regression · Odds · Odds ratio · Pathology · Population · Selection bias · Statistics · Advanced Causal Inference Techniques · Chronic Disease Management Strategies · Genetic Associations and Epidemiology · Internal Medicine · Mathematics · Medicine · Psychology

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Citations per year0.15
Citation span2013 - 2015 (3)
Highly citedNo

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