title: Generalized linear mixed models for multi-reader multi-case studies of diagnostic tests.
abstract: Diagnostic tests are often compared in multi-reader multi-case (MRMC) studies in which a number of cases (subjects with or without the disease in question) are examined by several readers using all tests to be compared. One of the commonly used methods for analyzing MRMC data is the Obuchowski-Rockette (OR) method, which assumes that the true area under the receiver operating characteristic curve (AUC) for each combination of reader and test follows a linear mixed model with fixed effects for test and random effects for reader and the reader-test interaction. This article proposes generalized linear mixed models which generalize the OR model by incorporating a range-appropriate link function that constrains the true AUCs to the unit interval. The proposed models can be estimated by maximizing a pseudo-likelihood based on the approximate normality of AUC estimates. A Monte Carlo expectation-maximization algorithm can be used to maximize the pseudo-likelihood, and a non-parametric bootstrap procedure can be used for inference. The proposed method is evaluated in a simulation study and applied to an MRMC study of breast cancer detection.
authors.affiliation: 1 Department of Mathematics, Harbin Institute of Technology, Harbin, P. R. China.|2 Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Wei
authors.initials: W
authors.lastname: Liu
authors.affiliation: 2 Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Norberto
authors.initials: N
authors.lastname: Pantoja-Galicia
authors.affiliation: 2 Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Bo
authors.initials: B
authors.lastname: Zhang
authors.affiliation: 2 Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Richard M
authors.initials: RM
authors.lastname: Kotz
authors.affiliation: 2 Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Gene
authors.initials: G
authors.lastname: Pennello
authors.affiliation: 3 Department of Biostatistics, St Jude Children's Research Hospital, Memphis, Tennessee, USA.
authors.forename: Hui
authors.initials: H
authors.lastname: Zhang
authors.affiliation: 4 Medical Affairs, GE Healthcare, Milwaukee, Wisconsin, USA.
authors.forename: Jessie
authors.initials: J
authors.lastname: Jacob
authors.affiliation: 2 Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Zhiwei
authors.initials: Z
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001943:Breast Neoplasms / Q000175:diagnosis*; D003955:Diagnostic Tests, Routine / Q000379:methods*; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D009010:Monte Carlo Method; D012372:ROC Curve
keywords: AUC; EM algorithm; ROC curve; diagnostic medicine; pseudo-likelihood; random effect