Assessing the Impact of Misclassification when Comparing Prevalence Data: A Novel Sensitivity Analysis Approach
- *Corresponding Author:
- Ninet Sinaii
Biostatistics and Clinical Epidemiology Service
CC, NIH, Bethesda, MD, USA
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.
A simple sensitivity analysis technique was developed to assess the impact of misclassification and verify observed prevalence differences between distinct populations.
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.
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%).
This sensitivity analysis enabled us to verify observed prevalence differences. This useful, simple approach is for comparing prevalence estimates between distinct populations.