Sample size calculations for ROC studies: Parametric robustness and Bayesian nonparametrics

Statistics in Medicine

Volumn 31, Issue 2, 2012, Pages 131-142

  Cheng, Dunlei ^a   Branscum, Adam J ^b   Johnson, Wesley O ^c  

^a BAYLOR HEALTH CARE SYSTEM   (United States)

^b OREGON STATE UNIVERSITY   (United States)

^c 5226 Donald Bren Hall   (United States)

Author keywords

AUC; Bayesian design; Dirichlet process mixtures; ROC curve; Simulation

Indexed keywords

ARTICLE; BAYES THEOREM; CALCULATION; COMPUTER PROGRAM; CONTROLLED STUDY; DATA ANALYSIS; NONHUMAN; NONPARAMETRIC TEST; PARAMETRIC TEST; RECEIVER OPERATING CHARACTERISTIC; SAMPLE SIZE; SCORING SYSTEM; SENSITIVITY ANALYSIS; SIMULATION; STATISTICAL MODEL; STUDY DESIGN;

BAYES THEOREM; HUMANS; ROC CURVE; SAMPLE SIZE; STATISTICS, NONPARAMETRIC;

EID: 84855231488     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.4396     Document Type: Article

References (18)

1. Obuchowski NA, McClish DK. Sample size determination for diagnostic accuracy studies involving two-group normal ROC curve indices. Statistics in Medicine 1997; 16:1529-1542.
2. Cheng D, Branscum AJ, Stamey JD. A Bayesian approach to sample size determination for studies designed to evaluate continuous medical tests. Computational Statistics and Data Analysis 2010; 54:298-307.
3. Hanson TE, Branscum AJ, Gardner IA. Multivariate mixtures of Polya trees for modeling ROC data. Statistical Modeling 2008; 8:81-96.
4. Wang F, Gatsonis CA. Hierarchical models for ROC curve summary measure: design and analysis of multi-reader, multi-modality studies of medical test. Statistics in Medicine 2008; 27:243-256.
5. Dendukuri N, Rahme E, Bélisle P, Joseph L. Bayesian sample size determination for prevalence and diagnostic test studies in the absence of a gold standard test. Biometrics 2004; 60:388-387.
6. Stamey JD, Seaman JW, Young DM. Bayesian sample-size determination for inference on two binomial populations with no gold standard classifier. Statistics in Medicine 2005; 24:2963-2976.
7. Qin G, Zhou X-H. Empirical likelihood inference for the area under the ROC curve. Biometrics 2006; 62:613-622.
8. Pepe MS. The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press: Oxford, 2003.
9. 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.
10. Lloyd CJ. Using smoothed receiver operating characteristic curves to summarize and compare diagnostic systems. Journal of the American Statistical Association 1998; 93:1356-1364.
11. Erkanli A, Sung M, Costello EJ, Angold A. Bayesian semi-parametric ROC analysis. Statistics in Medicine 2006; 25:3905-3928.
12. Hanson TE, Kottas A, Branscum AJ. Modelling stochastic order in the analysis of receiver operating characteristic data: Bayesian nonparametric approaches. Applied Statistics 2008; 57:207-225.
13. Branscum AJ, Johnson WO, Hanson TE, Gardner IA. Bayesian semiparametric ROC curve estimation and disease diagnosis. Statistics in Medicine 2008; 27:2474-2496.
14. Sethuraman J. A constructive definition of Dirichlet priors. Statistica Sinica 1994; 4:639-650.
15. Ishwaran H, James LF. Gibbs sampling methods for stick-breaking priors. Journal of the American Statistical Association 2001; 96:161-173.
16. Neal RM. Markov chain sampling methods for Dirichlet process mixture models. Journal of Computational and Graphical Statistics 2000; 9:249-265.
17. Hanson TE, Branscum AJ, Johnson WO. Bayesian nonparametric modeling and data analysis: an introduction. In Handbook of Statistics, Vol. 25, Dey DK, Rao CR (eds). Elsevier: North-Holland, 2005; 245-278.
18. Wang F, Gelfand AE. A simulation-based approach to Bayesian sample size determination for performance under a given model and for separating models. Statistical Science 2002; 17:193-208.