When does combining markers improve classification performance and what are implications for practice?

Statistics in Medicine

Volumn 32, Issue 11, 2013, Pages 1877-1892

  Bansal, Aasthaa ^a   SullivanPepe, Margaret ^b  

^a UNIVERSITY OF WASHINGTON   (United States)

^b USA   (United States)

Author keywords

Diagnosis; Incremental value; Marker combination; ROC curve; Sensitivity; Specificity

Indexed keywords

BIOLOGICAL MARKER;

ARTICLE; BIOLOGICAL MODEL; CLASSIFICATION; CLINICAL PRACTICE; CONTROLLED STUDY; CORRELATION ANALYSIS; HUMAN; OUTCOME ASSESSMENT; PREDICTION; SIMULATION; STANDARD; STATISTICAL MODEL;

BIOLOGICAL MARKERS; DATA INTERPRETATION, STATISTICAL; HUMANS; MODELS, STATISTICAL; ROC CURVE; SENSITIVITY AND SPECIFICITY;

EID: 84877127225     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.5736     Document Type: Article

References (13)

1. Pepe MS, Janes H, Longton G, Leisenring W, Newcomb P. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Americal Journal of Epidemiology 2004; 159:882-890. DOI: 10.1093/aje/kwh101.
2. Anderson GL, McIntosh M, Wu L, Barnett M, Goodman G, Thorpe JD, Bergan L, Thornquist MD, Scholler N, Kim N, O'Briant K, Drescher C, Urban N. Assessing lead time of selected ovarian cancer biomarkers: a nested case-control study. Journal of the National Cancer Institute 2010; 102:26-38. DOI: 10.1093/jnci/djp438.
3. Gail MH. Discriminatory accuracy from single-nucleotide polymorphisms in models to predict breast cancer risk. Journal of the National Cancer Institute 2008; 100:1037-1041. DOI: 10.1093/jnci/djn180.
4. McIntosh MW, Pepe MS. Combining several screening tests: optimality of the risk score. Biometrics 2002; 58:657-664. DOI: 10.1111/j.0006-341X.2002.00657.x.
5. Neyman J, Pearson ES. On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society London A 1933; 24:289-337. DOI: 10.1098/rsta.1933.0009.
6. Metz CE, Kronman HB. Statistical significance tests for binormal ROC curves. Journal of Mathematical Psychology 1980; 22:218-243. DOI: 10.1016/0022-2496(80)90020-6.
7. Pepe MS. The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press: Oxford, 2003. 81-85. DOI: 10.1111/j.1467-985X.2006.00430_9.x.
8. Guyon I, Elisseeff A. An introduction to variable and feature selection. Journal of Machine Learning Research 2003; 3:1157-1182. DOI: 10.1162/153244303322753616.
9. Berger AP, Deibl M, Steiner H, Bektic J, Pelzer A, Spranger R, Klocker H, Bartsch G, Horninger W. Longitudinal PSA changes in men with and without prostate cancer: assessment of prostate cancer risk. The Prostate 2005; 64:240-245. DOI: 10.1002/pros.20210.
10. McIntosh MW, Urban N, Karlan B. Generating longitudinal screening algorithms using novel biomarkers for disease. Cancer Epidemiology, Biomarkers & Prevention 2002; 11:159-166.
11. Skates SJ, Xu FJ, Yu YH, Sjovall K, Einhorn N, Chang Y, Bast R, Jr, Knapp R. Toward an optimal algorithm for ovarian cancer screening with longitudinal tumor markers. Cancer Supplement 1995; 76(10):2004-2010. DOI: 10.1002/1097-0142(19951115)76:10+<2004::AID-CNCR2820761317>3.0.CO;2-G.
12. Skates CJ, Pauler DK. Screening based on the risk of cancer calculation from Bayesian hierarchical change-point models of longitudinal markers. Journal of the American Statistical Association 2001; 96:429-439. DOI: 10.1198/016214501753168145.
13. Hosmer DW, Lemeshow S. Applied Logistic Regression. John Wiley & Sons, Inc.: New York, 1989. Section 5.2.2. DOI: 10.1002/sim.4780100718.