|Title||Assessing the dependence of sensitivity and specificity on prevalence in meta-analysis.|
|Publication Type||Journal Article|
|Year of Publication||2011|
|Authors||Li, Jialiang, and Jason P. Fine|
|Date Published||2011 Oct|
|Keywords||Biomarkers, Tumor, Biostatistics, CA-125 Antigen, Diagnostic Imaging, Female, Humans, Meta-Analysis as Topic, Models, Statistical, Neoplasms, Ovarian Neoplasms, Positron-Emission Tomography, Prevalence, Sensitivity and Specificity, Tomography, X-Ray Computed|
We consider modeling the dependence of sensitivity and specificity on the disease prevalence in diagnostic accuracy studies. Many meta-analyses compare test accuracy across studies and fail to incorporate the possible connection between the accuracy measures and the prevalence. We propose a Pearson type correlation coefficient and an estimating equation-based regression framework to help understand such a practical dependence. The results we derive may then be used to better interpret the results from meta-analyses. In the biomedical examples analyzed in this paper, the diagnostic accuracy of biomarkers are shown to be associated with prevalence, providing insights into the utility of these biomarkers in low- and high-prevalence populations.
|Original Publication||Assessing the dependence of sensitivity and specificity on prevalence in meta-analysis.|
|PubMed Central ID||PMC4042906|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
P30 AI050410 / AI / NIAID NIH HHS / United States
R01 CA094893 / CA / NCI NIH HHS / United States
Assessing the dependence of sensitivity and specificity on prevalence in meta-analysis.