Bayesian semi-parametric ROC analysis

Statistics in Medicine

Volumn 25, Issue 22, 2006, Pages 3905-3928

  Erkanli, Alaattin ^a   Sung, Minje ^b   Costello, E Jane ^a   Angold, Adrian ^a  

^a DUKE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b Ajou University   (South Korea)

Author keywords

Diagnostic test; Gibbs sampling; Kernel density estimation; Mixtures of Dirichlet process priors; ROC curve; Semi parametric Bayesian methods

Indexed keywords

ADOLESCENT; ALGORITHM; ARTICLE; BAYES THEOREM; DIAGNOSTIC TEST; HUMAN; MATHEMATICAL COMPUTING; NORMAL DISTRIBUTION; PARAMETRIC TEST; RECEIVER OPERATING CHARACTERISTIC; SAMPLE (STATISTICS); SAMPLING; SCHOOL CHILD; SENSITIVITY AND SPECIFICITY;

ADOLESCENT; BAYES THEOREM; CHILD; COMPUTER SIMULATION; DEPRESSION; DIAGNOSTIC TESTS, ROUTINE; HUMANS; MARKOV CHAINS; MODELS, STATISTICAL; MONTE CARLO METHOD; ROC CURVE;

EID: 33750861295     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.2496     Document Type: Article

References (41)

1. Costello EJ, Angold A, Burns BJ, Stangl DK, Tweed DL, Erkanli A, Worthman CM. The great smoky mountains study of youth: goals, designs, methods, and the prevalence of DSM-III-R disorders. Archives of General Psychiatry 1996; 53:1129-1136.
2. Hanley JA. The use of the binormal model for parametric ROC analysis of quantitative diagnostic tests. Statistics in Medicine 1996; 15:1575-1585.
3. Zou KH, Hall WJ, Shapiro DE. Smooth non-parametric receiver operating characteristic (ROC) curves for continuous diagnostic tests. Statistics in Medicine 1997; 16:2143-2156.
4. Metz CE, Shen J, Wang P, Kronman HB. FORTRAN Program LABROC4. Department of Radiology, University of Chicago, 1995.
5. Sorribas A, March J, Trujillano J. A new parametric method based on S-distribution for computing receiver operating characteristic curves for continuous diagnostic tests. Statistics in Medicine 2002; 21:1213-1235.
6. Viot EO. The S-distribution: a tool for approximation and classification of univariate, unimodal probability distributions. Biometrical Journal 1992; 7:855-878.
7. Lloyd CJ. Using smoothed receiver operating characteristic curves to summarize and compare diagnostic systems. Journal of the American Statistical Association 1998; 93(444):1356-1998.
8. Silverman BW. Density Estimation for Statistics and Data Analysis. Chapman & Hall: New York, 1985.
9. Gasser T, Müller HG. Estimating regression functions and their derivatives by the kernel method. Scandinavian Journal of Statistics 1984; 11:171-185.
10. Green DM, Swets JA. Signal Detection Theory and Psychophysics. Wiley: New York, 1966. Reprinted by Peninsula Publishing: Los Altos Hills, CA, 1988.
11. Ferguson TS. Bayesian density estimation by mixtures of normal distributions. In Recent Advances in Statistics, Rizvi H, Rustagi J (eds). Academic Press: New York, 1983; 287-302.
12. Escobar MD, West M. Bayesian density estimation and inference using mixtures. Journal of the American Statistical Association 1995; 90:577-588.
13. Ferguson TS. A Bayesian analysis of some nonparametric problems. Annals of Statistics 1973; 1:209-230.
14. Müller P, Erkanli A, West M. Bayesian curve fitting using multivariate normal mixtures. Biometrika 1996; 83(1):67-79.
15. Antoniak CE. Mixtures of Dirichlet processes with applications to non-parametric problems. Journal of the American Statistical Association 1974; 2:1152-1174.
16. Casella G, George EI. Explaining the Gibbs sampler. American Statistician 1992; 46:167-174.
17. Spiegelhalter D, Thomas A, Best N, Gilks W. Bayesian Inference Using Gibbs Sampling Manual. MRC Biostatistics Unit, Cambridge University: Cambridge, MA, 1996.
18. Gelfand AE. Approaches to semiparametric Bayesian regression. In Asymptotics, Nonparametrics and Time Series, Subir G (ed). Marcel Dekker Inc.: New York, 1998; 615-638.
19. Erkanli A, Müller P, Stangl D. A Bayesian analysis of ordinal data using mixtures. Presented at the American Statistical Association 24th Annual Proceedings, San Francisco, CA, 1993.
20. Kuo L, Mallick B. Bayesian semiparametric inference for the accelerated failure time model. Canadian Journal of Statistics 1997; 25:457-472.
21. West M, Müller P, Escobar MD. Hierarchical priors and mixture models, with application in regression and density estimation. In Aspects of Uncertainty: A Tribute, Lindley DV, Smith AFM, Freeman P (eds). Wiley: New York, 1994; 363-386.
22. Escobar MD, West M. Computing Bayesian nonparametric hierarchical models. In Practical Nonparametric and Semiparametric Bayesian Statistics, Dey D, Mueller P, Sinha D (eds). Springer: New York, 1998; 1-22.
23. Kleinman K, Ibrahim JG. A semi-parametric Bayesian approach to the random effects model. Biometrics 1998; 54:921-938.
24. Ishwaran H, James LF. Approximate Dirichlet process computing in finite normal mixtures: smoothing and prior information. Journal of Computational and Graphical Statistics 2002; 11:508-532.
25. Liu JS. Nonparametric hierarchical Bayes via sequential imputations. Annals of Statistics 1996; 24:911-930.
26. Microsoft Corp. Microsoft Excel 2002. 2002.
27. Brooks S, Gelman A. General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 1998; 7:434-455.
28. Insightful Corp. S-plus 6.2. 2003.
29. Achenbach TM. Integrative Guide for the 1991 CBCL/4-18 YSR and TRF Profiles. Department of Psychiatry, University of Vermont: Burlington, 1991.
30. Angold A, Costello EJ. A test-retest reliability study of child-reported psychiatric symptoms and diagnoses using the child and adolescent psychiatric assessment (CAPA-C). Psychological Medicine 1995; 25:755-762.
31. APA (American Psychiatric Association). Diagnostic and Statistical Manual of Mental Disorders (4th edn) (DSM-IV). APA: Washington, DC, 1994.
32. Qu Y, Tang M, Kutner MH. Random effects models in latent class analysis for evaluating accuracy of diagnostic tests. Biometrics 1996; 52:797-810.
33. Yang I, Becker MP. Latent variable modelling of diagnostic accuracy. Biometrics 1997; 53:948-958.
34. Berkson J. Are there two regressions?. Journal of the American Statistical Association 1950; 45:164-180.
35. Sethuraman J. A constructive definition of Dirichlet priors. Statistica Sinica 1994; 4:639-650.
36. Sethuraman J, Tiwari RC. Convergence of Dirichlet measures and interpretation of their parameters. Statistical Decision Theory and Related Topics III 1982; 2:305-315.
37. Escobar MD. Estimating normal means with a Dirichlet process prior. Journal of the American Statistical Association 1994; 89:268-277.
38. MacEachern SN, Müller P. Estimating mixtures of Dirichlet process models. Journal of Computational and Graphical Statistics 1998; 2:223-238.
39. Ishwaran H, James LF, Sun J. Bayesian model selection in finite mixtures by marginal density decompositions. Journal of the American Statistical Association 2001; 96:1316-1332.
40. Ishwaran H, James LF. Gibbs sampling methods for stick-breaking priors. Journal of American Statistical Association 2001; 96:161-173.
41. Congdon P. Bayesian Statistical Modelling. Wiley: Chichester, 2001.