ISSN: 2161-1165

Epidemiology: Open Access
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  • Research Article   
  • Epidemiol 2014, Vol 4(3): 155
  • DOI: 10.4172/2161-1165.1000155

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, Email: sinaiin@mail.nih.gov

Received Date: Jan 11, 2014 / Accepted Date: Apr 25, 2014 / Published Date: Apr 30, 2014

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: Epidemiology; Comorbid diseases; Distinct populations

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.

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