Armando Baena1, Isabel Cristina Garcés-Palacio2, Hugo Grisales3. 1. Infection and Cancer Group, School of Medicine, Universidad de Antioquia, Medellín, Colombia. 2. Epidemiology Group, National School of Public Health, Universidad de Antioquia, Medellín, Colombia. 3. Demography and Health Group, National School of Public Health, Universidad de Antioquia, Medellín, Colombia.
Abstract
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.
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.
Authors: Maria Pyra; Jairam R Lingappa; Renee Heffron; David W Erikson; Steven W Blue; Rena C Patel; Kavita Nanda; Helen Rees; Nelly R Mugo; Nicole L Davis; Athena P Kourtis; Jared M Baeten Journal: Contraception Date: 2018-02-17 Impact factor: 3.375
Authors: Vincent Were; Louise Foley; Eleanor Turner-Moss; Ebele Mogo; Pamela Wadende; Rosemary Musuva; Charles Obonyo Journal: Int J Equity Health Date: 2022-04-09