title: Comparison of operational characteristics for binary tests with clustered data.
abstract: Although statistical methodology is well-developed for comparing diagnostic tests in terms of their sensitivity and specificity, comparative inference about predictive values is not. In this paper, we consider the analysis of studies comparing operating characteristics of two diagnostic tests that are measured on all subjects and have test outcomes from multiple sites with varying number of sites among subjects. We have developed a new approach for comparing sensitivity, specificity, positive predictive value, and negative predictive value with simple variance calculation and, in particular, focus on comparing tests using difference of positive and negative predictive values. Simulation studies are conducted to show the performance of our approach. We analyze real data on patients with lung cancer, based on their diagnostic tests, to illustrate the methodology.
authors: Department of Statistics, Yeungnam University Gyeongsan, Gyeongbuk, 712-749, South Korea.
authors: Minjung
authors: M
authors: Kwak
authors: Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 135-710, South Korea.
authors: Sang-Won
authors: SW
authors: Um
authors: Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, U.S.A.
authors: Sin-Ho
authors: SH
authors: Jung
mesh_terms: D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D003955:Diagnostic Tests, Routine; D006801:Humans; D015233:Models, Statistical; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity
keywords: clustered binary outcome; negative predictive value; positive predictive value; sensitivity; specificity