Multireader multicase variance analysis for binary data.

Multireader multicase (MRMC) variance analysis has become widely utilized to analyze observer studies for which the summary measure is the area under the receiver operating characteristic (ROC) curve. We extend MRMC variance analysis to binary data and also to generic study designs in which every reader may not interpret every case. A subset of the fundamental moments central to MRMC variance analysis of the area under the ROC curve (AUC) is found to be required. Through multiple simulation configurations, we compare our unbiased variance estimates to naïve estimates across a range of study designs, average percent correct, and numbers of readers and cases.

[1]  Brandon D Gallas,et al.  One-shot estimate of MRMC variance: AUC. , 2006, Academic radiology.

[2]  Matthew A. Kupinski,et al.  Probabilistic foundations of the MRMC method , 2005, SPIE Medical Imaging.

[3]  M. Pepe The Statistical Evaluation of Medical Tests for Classification and Prediction , 2003 .

[4]  Murray H. Loew,et al.  Assessing Classifiers from Two Independent Data Sets Using ROC Analysis: A Nonparametric Approach , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  R. F. Wagner,et al.  Multireader, multicase receiver operating characteristic analysis: an empirical comparison of five methods. , 2004, Academic radiology.

[6]  K. Berbaum,et al.  Receiver operating characteristic rating analysis. Generalization to the population of readers and patients with the jackknife method. , 1992, Investigative radiology.

[7]  Eric Clarkson,et al.  A probabilistic model for the MRMC method, part 1: theoretical development. , 2006, Academic radiology.

[8]  C E Metz,et al.  Variance-component modeling in the analysis of receiver operating characteristic index estimates. , 1997, Academic radiology.

[9]  Stephen L Hillis,et al.  Monte Carlo validation of the Dorfman-Berbaum-Metz method using normalized pseudovalues and less data-based model simplification. , 2005, Academic radiology.

[10]  Mark Schiffman,et al.  ASCUS-LSIL Triage Study , 2000, Acta Cytologica.

[11]  Nancy A Obuchowski,et al.  A comparison of the Dorfman–Berbaum–Metz and Obuchowski–Rockette methods for receiver operating characteristic (ROC) data , 2005, Statistics in medicine.

[12]  Xiao Song,et al.  A marginal model approach for analysis of multi-reader multi-test receiver operating characteristic (ROC) data. , 2005, Biostatistics.

[13]  David G. Brown,et al.  Reader studies for validation of CAD systems , 2008, Neural Networks.

[14]  Mark Schiffman,et al.  Visual appearance of the uterine cervix: correlation with human papillomavirus detection and type. , 2007, American journal of obstetrics and gynecology.

[15]  R. F. Wagner,et al.  Components-of-variance models and multiple-bootstrap experiments: an alternative method for random-effects, receiver operating characteristic analysis. , 2000, Academic radiology.