Mistakes in validating the accuracy of a prediction classifier in high-dimensional but small-sample microarray data

Statistical Methods in Medical Research

Volumn 17, Issue 6, 2008, Pages 635-642

  Lee, Sunho ^a  

^a Sejong University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; CLASSIFICATION; CLASSIFIER; CLINICAL PRACTICE; ERROR; GENE EXPRESSION; GENE NUMBER; HUMAN; MICROARRAY ANALYSIS; PREDICTION; REPRODUCIBILITY; SAMPLE SIZE; VALIDATION PROCESS;

ANTINEOPLASTIC AGENTS, HORMONAL; BIOMETRY; BREAST NEOPLASMS; DATA INTERPRETATION, STATISTICAL; FEMALE; GENE EXPRESSION PROFILING; HOMEODOMAIN PROTEINS; HUMANS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; RECEPTORS, INTERLEUKIN; TAMOXIFEN;

EID: 58749086251     PISSN: 09622802     EISSN: None     Source Type: Journal    
DOI: 10.1177/0962280207084839     Document Type: Article

References (26)

1. Dudoit S., Fridlyand J., Speed TP Comparison of discrimination methods for the classification of tumors using gene-expression data. Journal of the American Statistical Association 2002; 97 (457): 77 - 87.
2. Shieh G., Jiang YC, Shih Y. Comparison of support vector machines to other classifiers using gene expression data. Communications in statistics Part B, Simulation and Computation 2006; 35 (1): 241 - 56.
3. Fu LM, Fu-Liu CS Multi-class cancer subtype classification based on gene expression signatures with reliability analysis. FEBS letters 2004; 561: 186 - 90.
4. Ambroise C., McLachlan GJ Selection bias in gene extraction on the basis of microarray gene-expression data. Proceedings of National Academy of Sciences USA 2002; 99 (10): 562 - 6.
5. Ransohoff DF Opinion: Rules of evidence for cancer molecular-marker discovery and validation. Nature reviews: Cancer 2004; 4 (4): 309 - 14.
6. Simon R., Radmacher MD, Dobbin K., McShane LM Pirfalls in the use of DNA microarray data for diagnostic and prognostic classification. Journal of National Cancer Institutes 2003; 95 (1): 14 - 8.
7. Dougherty ER Small sample issues for microarray-based classification. Comparative and Functional Genomics 2001; 2: 28 - 34.
8. Molinaro AM, Simon R., Pfeiffer RM Prediction error estimation: A comparison of resampling methods. Bioinformatics 2005; 21 (15): 3301 - 07.
9. Wood IA, Visscher PM, Mengersen KL Classification based upon gene expression data: Bias and precision of error rates. Bioinformatics 2007; 23 (11): 1363 - 70.
10. McLachlan GJ, Do KA, Ambroise C. Analyzing microarray gene expression data. Wiley, 2004.
11. Braga-Neto UM, Dougherty ER Is cross-validation valid for small-sample microarray classification? Bioinformatics 2004; 20 (3): 374 - 80.
12. Rosenwald A., Wright G., Chan WC, Connors JM, Campo E., Fisher RI et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large B-cell lymphoma. The New England Journal of Medicine 2002; 346 (25): 1937 - 47.
13. Zhang H., Yu CU, Singer B., Xiong M. Recursive partitioning for tumor classification with gene expression microarray data. Proceedings of National Academy of Sciences, USA 2001; 98 (12): 6730 - 35.
14. van't Veer LJ, Dai H., van de Vijver MJ, He YD, Hart AA, Mao M., Peterse H. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002; 415 (31): 530 - 6.
15. van de Vijver MJ, He YD, van't Veer LJ, Dai H., Hart AAM, Voskuil DW et al. A gene-expression signature as a predictor of survival in breast cancer. The New England Journal of Medicine 2002; 347 (25): 1999 - 2009.
16. Bair E., Tibshirani R. Semi-supervised methods to predict patient survival from gene expression data. PLOS biology 2004; 2 (4): 511 - 22.
17. Ma XJ, Wang Z., Ryan PD, Isakoff SJ, Barmettler A., Fuller A., Muir BM A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell 2004; 5: 607 - 16.
18. Reid JF, Lusa L., De Cecco L., Coradini D., Veneroni S., Daidone M. Limits of predictive models using microarray data for breast cancer clinical treatment outcome. Journal of National Cancer Institute 2005; 97: 927 - 30.
19. Simon R. Development and validation of therapeutically relevant multi-gene biomarker classifiers. Journal of National Cancer Institute 2005; 97: 866 - 7.
20. Mhawech P., Greloz V., Oppikofer C., Szalay-Quinodoz I., Herrmann F. Expression of cell cycle proteins in T1a and T1b urothelial bladder carcinoma and their value in predicting tumor progression. Cancer 2004; 100 (11): 2367 - 75.
21. Lusa L., McShane LM, Radmacher MD, Shih JH, Wright GW, Simon R. Appropriateness of inference procedures based on within-sample validation for assessing gene expression microarray-based prognostic classifier performance. Statistics in medicine 2007; 26: 1102 - 13.
22. Pepe MS, Janes H., Longton G., Leisenring W., Newcomb P. Limitations of the odds ratio in gauging performance of a diagnostic, prognostic, or screening marker. American Journal of Epidemiology 2004; 159 (9): 882 - 90.
23. Radmacher MD, McShane LM, Simon R. A paradigm for class prediction using gene expression profiles. Journal of Computational Biology 2002; 9 (3): 505 - 12.
24. Dave SS, Wright G., Tan B., Rosenwald A., Gascoyne RD, Chan WC et al. Predicting survival in follicular lymphoma by features of tumor-Infiltrating Immune cells. The New England Journal of Medicine 2004; 351 (21): 2159 - 69.
25. Tibshirani R., Hong WJ, Warnke R., Chu G., Staudt LM, Wright G., Dave S. Immune signatures in follicular lymphoma. The New England Journal of Medicine 2005; 352: 1496 - 7.
26. Tibshirani, R. Re-analysis of Dave et al, NEJM Nov 2004; 18: http://www.stat.stanford.edu/~tibs/FL/report.txt