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 |
Journal | Biostatistics |
Volume | 12 |
Issue | 4 |
Pagination | 710-22 |
Date Published | 2011 Oct |
ISSN | 1468-4357 |
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 |
Abstract | 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. |
DOI | 10.1093/biostatistics/kxr008 |
Alternate Journal | Biostatistics |
Original Publication | Assessing the dependence of sensitivity and specificity on prevalence in meta-analysis. |
PubMed ID | 21525421 |
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.
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