The effect of misclassification error on risk estimation in case-control studies

Introduction: In epidemiological studies, misclassification error, especially differential misclassification, has serious implications. Objective: To illustrate how differential misclassification error (DME) and non-differential misclassification error (NDME) occur in a case-control design and to de...

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Autores:
Garcés Palacio, Isabel Cristina
Grisales Romero, Hugo de Jesús
Baena Zapata, Armando
Tipo de recurso:
Article of investigation
Fecha de publicación:
2015
Institución:
Universidad de Antioquia
Repositorio:
Repositorio UdeA
Idioma:
eng
OAI Identifier:
oai:bibliotecadigital.udea.edu.co:10495/47655
Acceso en línea:
https://hdl.handle.net/10495/47655
Palabra clave:
Clasificación
Classification
Sesgo
Bias
Estudios de Casos y Controles
Case-Control Studies
Oportunidad Relativa
Odds Ratio
Sensibilidad y Especificidad
Sensitivity and Specificity
Simulación por Computador
Computer Simulation
https://id.nlm.nih.gov/mesh/D002965
https://id.nlm.nih.gov/mesh/D015982
https://id.nlm.nih.gov/mesh/D016022
https://id.nlm.nih.gov/mesh/D016017
https://id.nlm.nih.gov/mesh/D012680
https://id.nlm.nih.gov/mesh/D003198
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Rights
openAccess
License
https://creativecommons.org/licenses/by/4.0/
Description
Summary:Introduction: In epidemiological studies, misclassification error, especially differential misclassification, has serious implications. Objective: To illustrate how differential misclassification error (DME) and non-differential misclassification error (NDME) occur in a case-control design and to describe the trends in DME and NDME. Methods: Different sensitivity levels, specificity levels, prevalence rates and odds ratios were simulated. Interaction graphics were constructed to study bias in the different settings, and the effect of the different factors on bias was described using linear models. Results: One hundred per cent of the biases caused by NDME were negative. DME biased the association positively more often than it did negatively (70 versus 30%), increasing or decreasing the OR estimate towards the null hypothesis. Conclusions: The effect of the sensitivity and specificity in classifying exposure, the prevalence of exposure in controls and true OR differed between positive and negative biases. The use of valid exposure classification instruments with high sensitivity and high specificity is recommended to mitigate this type of bias.