A Bayesian hierarchical approach to multirater correlated ROC analysis

Statistics in Medicine

Volumn 25, Issue 11, 2006, Pages 1858-1871

  Johnson, Timothy D ^a   Johnson, Valen E ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Area under the ROC curve; Bayesian analysis; Hierarchical model; Receiver operating characteristics; ROC analysis

Indexed keywords

ANALYSIS OF VARIANCE; ANALYTICAL ERROR; AREA UNDER THE CURVE; ARTICLE; BAYES THEOREM; CONTROLLED STUDY; CORRELATION ANALYSIS; DIAGNOSTIC ACCURACY; DIAGNOSTIC PROCEDURE; DIAGNOSTIC VALUE; HUMAN; LUNG NODULE; MATHEMATICAL COMPUTING; METHODOLOGY; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY ANALYSIS; SIMULATION; STATISTICAL MODEL; THORAX RADIOGRAPHY;

BAYES THEOREM; COMPUTER SIMULATION; HUMANS; LUNG DISEASES; MODELS, STATISTICAL; OBSERVER VARIATION; ROC CURVE;

EID: 33744818166     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.2314     Document Type: Article

References (18)

1. Dodd LE, Pepe MS. Semiparametric regression for the area under the receiver operating characteristic curve. Journal of the American Statistical Association 2003; 98(462):409-417.
2. Heagerty PJ, Lumley T, Pepe MS. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 2000; 56:337-341.
3. Ishwaran H, Gatsonis CA. A general class of hierarchical ordinal regression models with applications to correlated ROC analysis. Canadian Journal of Statistics 2000; 28(4):731-750.
4. Toledano AY, Gatsonis CA. Ordinal regression methodology for ROC curves derived from correlated data. Statistics in Medicine 1996; 15:1807-1826.
5. Pepe MS. Three approaches to regression analysis of receiver operating characteristic curves for continuous test results. Biometrics 1998; 54:124-135.
6. Dorfman DD, Berbaum KS, Metz CE. Receive operating characteristic rating analysis: generalization to the population of readers and patients with the jackknife method. Investigative Radiology 1992; 27:723-731.
7. Obuchowski NA, Rockette HE. Hypothesis testing of diagnostic accuracy for multiple readers and multiple tests: an ANOVA approach with dependent observations. Communication in Statistics-Simulation and Computation 1995; 24(2):285-308.
8. Beiden SV, Wagner RF, Campbell G. Components-of-variance models and multiple bootstrap experiments: an alternative method for random-effects, receiver operating characteristic analysis. Academic Radiology 2000; 7:341-349.
9. Metz CE, Herman BA, Roe C. A new approach for testing the significant differences between ROC curves measured from correlated data. In Information Processing in Medical Imaging, Deconinck F (ed.). Nijihoff: The Hague, The Netherlands, 1984; 432-445.
10. Bamber D. The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. Journal of Mathematical Psychology 1975; 12:387-415.
11. DeLong ER, Delong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparamettic approach. Biometrics 1988; 44:837-845.
12. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143:29-36.
13. Johnson VE, Albert JH. Ordinal Data Modeling. Springer: New York, 1999.
14. Efron B, Tibshirani RJ. An Introduction to the Bootstrap. Chapman & Hall: New York, 1993.
15. Swets JA, Pickett RM. Evaluation of Diagnostic Systems: Methods from Signal Detection Theory. Academic Press: New York, 1982.
16. Satterthwaite FE. An approximate distribution of estimates of variance components. Biometrics Bulletin 1946; 2:110-114.
17. Zhou X, Obuchowski NA, McClish DK. Statistical Methods in Diagnostic Medicine. Wiley: New York, 2002.
18. Pepe MS. The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press: Oxford, 2003.