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ISSN: 2161-1165

Epidemiology: Open Access
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Research Article

Assessing the Impact of Misclassification when Comparing Prevalence Data: A Novel Sensitivity Analysis Approach

Ninet Sinaii1,2*, Sean D Cleary3 and Pamela Stratton2

1Biostatistics and Clinical Epidemiology Service, CC, NIH, Bethesda, MD, USA

2Program in Reproductive and Adult Endocrinology, Eunice Kennedy Shriver NICHD, NIH, Bethesda, MD, USA

3Department of Epidemiology and Biostatistics, School of Public Health and Health Services, The George Washington University, Washington, DC, USA

*Corresponding Author:
Ninet Sinaii
Biostatistics and Clinical Epidemiology Service
CC, NIH, Bethesda, MD, USA
Tel: 301-402-9364
Fax: 301-496-0457
E-mail: [email protected]

Received date: January 11, 2014; Accepted date: April 25, 2014; Published date: April 30, 2014

Citation: Sinaii N, Cleary SD, Stratton P (2014) Assessing the Impact of Misclassification when Comparing Prevalence Data: A Novel Sensitivity Analysis Approach . Epidemiol 4:155. doi: 10.4172/2161-1165.1000155

Copyright: © 2014 Sinaii N, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background:
A simple sensitivity analysis technique was developed to assess the impact of misclassification and verify observed prevalence differences between distinct populations.

Methods:
The prevalence of self-reported comorbid diseases in 4,331 women with surgically-diagnosed endometriosis was compared to published clinical and population-based prevalence estimates. Disease prevalence misclassification was assessed by assuming over-reporting in the study sample and under-reporting in the general (comparison) population. Over- and under-reporting by 10%, 25%, 50%, 75%, and 90% was used to create a 5×5 table for each disease. The new prevalences represented by each table cell were compared by p-values, prevalence odds ratios, and 95% confidence intervals.

Results:
Three misclassification patterns were observed: 1) differences remained significant except at high degrees (>50%) of misclassification; 2) minimal (10%) misclassification negated any observed difference; and 3) with some (25-50%) misclassification, the difference disappeared, and the direction of significance changed at higher levels (>50%).

Conclusions:
This sensitivity analysis enabled us to verify observed prevalence differences. This useful, simple approach is for comparing prevalence estimates between distinct populations.

Keywords

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