Using the ROC curve for gauging treatment effect in clinical trials

Statistics in Medicine

Volumn 25, Issue 4, 2006, Pages 575-590

  Brumback, Lyndia C ^a   Pepe, Margaret S ^a,b   Alonzo, Todd A ^c  

^a UNIVERSITY OF WASHINGTON   (United States)

^b Fred Hutchinson Cancer Research Center   (United States)

^c UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

AUC regression; Covariate adjustment; Effect modification; Non parametric; Wilcoxon Mann Whitney test

Indexed keywords

PLACEBO; TOBRAMYCIN;

ARTICLE; CLINICAL TRIAL; COVARIANCE; CYSTIC FIBROSIS; HUMAN; RANK SUM TEST; REGRESSION ANALYSIS; ROC CURVE; STATISTICAL MODEL; STUDENT T TEST;

ADMINISTRATION, INHALATION; ADOLESCENT; ADULT; AGE FACTORS; ANTI-BACTERIAL AGENTS; CHILD; CLINICAL TRIALS, PHASE III; CYSTIC FIBROSIS; DATA INTERPRETATION, STATISTICAL; FEMALE; FORCED EXPIRATORY VOLUME; HUMANS; MALE; MULTICENTER STUDIES; RANDOMIZED CONTROLLED TRIALS; ROC CURVE; STATISTICS, NONPARAMETRIC; TOBRAMYCIN; TREATMENT OUTCOME;

EID: 31744449380     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.2345     Document Type: Article

References (31)

1. Wilcoxon F. Individual comparisons by ranking methods. Biometrics 1945; 1:80-83.
2. Mann H, Whitney D. On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics 1947; 18:50-60.
3. Armitage P. Importance of prognostic factors in the analysis of data from clinical trials. Controlled Clinical Trials 1981; 1:347-353.
4. Piantadosi S. Clinical Trials, A Methodologic Perspective. Wiley: New York, 1997.
5. Friedman L, Furberg C, DeMets D. Fundamentals of Clinical Trials. Springer: New York, 1998.
6. Cantin A. Cystic fibrosis lung inflammation: early, sustained, and severe. American Journal of Respiratory and Critical Care Medicine 1995; 151:939-941.
7. Ramsey BW, Pepe MS, Quan JM, Otto KL, Montgomery AB, Williams-Warren J, Vasiljev-k M, Borowitz D, Bowman CM, Marshall BC, Marshall S, Smith AL. Intermittent administration of inhaled Tobramycin in patients with cystic fibrosis. New England Journal of Medicine 1999; 340:23-30.
8. Hodges J, Lehman E. The efficacy of some nonparametric competitors of the t-test. Annals of Mathematical Statistics 1956; 27:324-335.
9. Blair R, Higgins J. A comparison of the power of Wilcoxon's rank-sum statistic to that of Student's t statistic under various non-normal distributions. Journal of Educational Statistics 1980; 5:309-335.
10. Wolfe D, Hogg R. On constructing statistics and reporting data. American Statistician 1971; 25:27-30.
11. O'Brien P. Comparing two samples: extensions of the t, rank-sum, and log-rank tests. Journal of the American Statistical Association 1988; 83:52-61.
12. Hollander M, Wolfe DA. Nonparametric Statistical Methods. Wiley: New York, 1999.
13. Hauck WW, Hyslop T, Anderson S. Generalized treatment effects for clinical trials. Statistics in Medicine 2000; 19:887-899.
14. Dodd LE, Pepe MS. Semiparametric regression for the area under the receiver operating characteristic curve. Journal of the American Statistical Association 2003; 98:409-417.
15. Boos DD, Brownie C. A rank-based mixed model approach to multisite clinical trials (Corr: 94V50 p322). Biometrics 1992; 48:61-72.
16. Lachin JM. Some large-sample distribution-free estimators and tests for multivariate partially incomplete data from two populations. Statistics in Medicine 1992; 11:1152-1170.
17. Pepe MS. The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press: New York, 2003.
18. Swets J, Pickett R. Evaluation of Diagnostic Systems: Methods from Signal Detection Theory. Academic Press: New York, 1982.
19. Hanley J. Receiver operating characteristic (ROC) methodology: the state of the art. Critical Reviews in Diagnostic Imaging 1989; 29:307-335.
20. Bamber D. The area above the ordinal dominance graph and the area below the receiver operating graph. Journal of Mathematical Psychology 1975; 12:387-415.
21. Hanley J, McNeil B. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143:29-36.
22. Hauck WW, Neuhaus JM, Kalbfleisch JD, Anderson S. A consequence of omitted covariates when estimating odds ratios. Journal of Clinical Epidemiology 1991; 44:77-81.
23. Hauck WW, Anderson S, Marcus SM. Should we adjust for covariates in nonlinear regression analyses of randomized trials? Controlled Clinical Trials 1998; 19:249-256.
24. Pocock SJ, Assmann SE, Enos LE, Kasten LE. Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems. Statistics in Medicine 2002; 21:2917-2930.
25. Rothwell PM. Subgroup analysis in randomized controlled trials: importance, indications, and interpretation. Lancet 2005; 365:176-186.
26. Tosteson A, Begg C. A general regression methodology for ROC curve estimation. Medical Decision Making 1988; 8:204-215.
27. Faraggi D. Adjusting receiver operating characteristic curves and related indices for covariates. The Statistician 2003; 52:179-192.
28. Pepe M. A regression modelling framework for receiver operating characteristic curves in medical diagnostic testing. Biometrika 1997; 84:595-608.
29. Pepe M, Cai T. The analysis of placement values for evaluating discriminatory measures. Biometrics 2004; 60:528-535.
30. Conover WJ, Iman RL. Rank transformations as a bridge between parametric and nonparametric statistics (C/R: p129-133). The American Statistician 1981; 35:124-129.
31. Conover WJ, Iman RL. Analysis of covariance using the rank transformation (Corr: V40 p1217). Biometrics 1982; 38:715-724.