Validation of biomarker-based risk prediction models

Clinical Cancer Research

Volumn 14, Issue 19, 2008, Pages 5977-5983

  Taylor, Jeremy M G ^a   Ankerst, Donna P ^b   Andridge, Rebecca R ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

^b UNIVERSITY OF TEXAS HEALTH SCIENCE CENTER AT SAN ANTONIO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER; PHARMACOLOGICAL BIOMARKER; TUMOR MARKER;

CANCER RESEARCH; CANCER RISK; GENE EXPRESSION; LUNG CANCER; PRIORITY JOURNAL; REVIEW; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; VALIDATION PROCESS; BIOMETRY; CLINICAL TRIAL; COHORT ANALYSIS; GENE EXPRESSION PROFILING; HUMAN; LUNG TUMOR; METABOLISM; METHODOLOGY; ONCOLOGY; RISK; STATISTICAL ANALYSIS; THEORETICAL MODEL; TREATMENT OUTCOME;

BIOMARKERS, PHARMACOLOGICAL; BIOMETRY; CLINICAL TRIALS AS TOPIC; COHORT STUDIES; DATA INTERPRETATION, STATISTICAL; GENE EXPRESSION PROFILING; HUMANS; LUNG NEOPLASMS; MEDICAL ONCOLOGY; MODELS, THEORETICAL; RESEARCH DESIGN; RISK; SENSITIVITY AND SPECIFICITY; TREATMENT OUTCOME; TUMOR MARKERS, BIOLOGICAL;

EID: 58149145635     PISSN: 10780432     EISSN: None     Source Type: Journal    
DOI: 10.1158/1078-0432.CCR-07-4534     Document Type: Review

References (43)

1. Thompson IM, Ankerst DP, Chi C, et al. Assessing prostate cancer risk: Results from the Prostate Cancer Prevention Trial. J Natl Cancer Inst 2006;98:529-34.
2. Gail MH, Brinton LA, Byar DP, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 1989;81:1879-86.
3. Kattan MW, Eastham JA, Stapleton AMF, Wheeler TM, Scardino PT. A preoperative nomogram for disease recurrence following radical prostatectomy for prostate cancer. J Natl Cancer Inst 1998;90:766-71.
4. Paik S, Shak S, Tang G, et al. A multi-gene assay to predict recurrence of tamoxifen-treated, nodenegative breast cancer. N Engl J Med 2004;351:2817-26.
5. Skates SJ, Pauler DK, Jacobs IJ. Screening based on the risk of cancer calculation from Bayesian hierarchical change point and mixture models of longitudinal markers. J Am Stat Assoc 2001;96:429-39.
6. Ransohoff DF. Lessons from controversy: ovarian cancer screening and serum proteomics. J Natl Cancer Inst 2005;97:315-9.
7. Baggerly KA, Morris JS, Edmonson SR, Coombes KR. Signal in noise: evaluating reported reproducibility ofs erum proteomic tests for ovarian cancer. J Natl Cancer Inst 2005;97:307-9.
8. Ioannidis JPA. Microarrays and molecular research: noise discovery? Lancet 2005;365:454-5.
9. Shedden K, Taylor JMG, Enkemann SA, et al. Gene expression-based survival prediction in lung adenocarcinoma: A multi-site, blinded validation study. Nat Med 2008;14:822-7.
10. Hayes DF, Bast RC, Desch CE, et al. Tumor marker utility grading system: a framework to evaluate clinical utility oft umor markers. J Natl Cancer Inst 1996;20:1456-66.
11. George SL. Statistical issues in translational cancer research. Clin Cancer Res. Vol.18. In press 2008.
12. Simon R. Using genomics in clinical trial design. Clin Cancer Res. Vol. 18. In press 2008.
13. Harrell FE. Regression modeling strategies with applications to linear models, logistic regression, and survival analysis. New York: Springer-Verlag; 2001.
14. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning;data-mining, inference, and prediction. New York: Springer; 2001.
15. Schwarzer G, Vach W, Schumacher M. On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Stat Med 2000;19:541-61.
16. Sargent DJ. Comparison of artificial neural networks with other statistical approaches: results from medical data sets. Cancer 2001;91:1636-42.
17. Ein-Dor L, Kela I, Getz G, Givol D, Domany E. Outcome signature genes in breast cancer: Is there a unique set? Bioinformatics 2005;21:171-8.
18. Owzar K, Barry WT, Jung S-H, Sohn I, George SL. Statistical challenges in pre-processing in microarray experiments in cancer. Clin Cancer Res. Vol. 18. In press 2008.
19. Hammond ME, Fitzgibbons PL, Compton CC, et al. College of American Pathologists Conference XXXV: solid tumor prognostic factors-which, how and so what? Summary document and recommendations for implementation. Cancer Committee and Conference Participants. Arch Pathol Lab Med 2000;124:958-65.
20. McShane LM, Altman DG, Sauerbrei W, et al. Reporting recommendations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst 2005;97:1180-4.
21. Ware JH. The limitations of risk factors as prognostic tools. N Engl J Med 2006;355:2615-7.
22. Pepe MS, Janes H, Longton G, Leisenring W, Newcomb P. Limitations oft he odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol 2004;159:882-90.
23. Chau CH, Rixe O, McLeod H, Figg WD. Validation of analytical methods for biomarkers employed in drug development. Clin Cancer Res. Vol.18. In press 2008.
24. Altman DG, Royston P. What do we mean by validating a prognostic model? Stat Med 2000;19:453-73.
25. Greenland S. The need for reorientation towards cost-effective prediction: Comments on Evaluating the added predicted ability ofa new marker: From area under the ROC curve to reclassification and beyond by M. J. Pencina et al. Stat Med 2008;27:199-206.
26. Gail MH, Costantino JP, Bryant J, et al. Weighing the risks and benefits of tamoxifen treatment for preventing breast cancer. J Natl Cancer Inst 1999;91:1829-46.
27. Sorlie T, Perou CM, Tibshirani R, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci US A 2001;98:10869-74.
28. Potti A, Dressman HK, Bild A, et al. Genomic signatures to guide the use of chemotherapeutics. Nat Med 2007;12:1294-300.
29. Coombes KR, Wang J, Baggerly KA. Microarrays: retracing steps. Nat Med 2007;13:1276-7.
30. Efron B, Tibshirani R. Improvements on cross-validation: The .632+ bootstrap method. J Am Stat Assoc 1997;92:548-60.
31. Constantino JP, Gail MH, Pee D, et al. Validation studies for models projecting the risk of invasive and total breast cancer incidence. J Natl Cancer Inst 1999;91:1541-8.
32. Parekh DJ, Ankerst DP, Higgins BA, et al. External validation of the Prostate Cancer Prevention Trial risk calculator in a screened population. Urology 2006;68:1152-5.
33. Han M, Humphreys EB, Hernandez DJ, Partin AW, Roehl KA, Catalona WJ. AUA abstract 1875: Comparison between the prostate cancer risk calculator and serum PSA. J Urol 2007;177:624.
34. Hernandez DJ, Han M, Humphreys EB, et al. AUA abstract 1874: External validation of the prostate cancer risk calculator. J Urol 2007;177:623.
35. Heagerty PJ, Zheng Y. Survival model predictive accuracy and ROC curves. Biometrics 2005;61:92-105.
36. Moskowitz CS, Pepe MS. Quantifying and comparing the predictive accuracy of continuous prognostic factors for binary outcomes. Biostatistics 2004;5:113-27.
37. Pepe MS, Feng Z, Huang Y, et al. Integrating the predictiveness of a marker with its performance as a classifier. UW Biostatistics Working Paper Series 2006; Working Paper 289.
38. Taylor JMG, Yu M, Sandler HM. Individualized predictions of disease progression following radiation therapy for prostate cancer. J Clin Oncol 2005;23:816-25.
39. Altman DG, Lausen B, Sauerbrei W, Schumacher M. Dangers of using "optimal" cutpoints in the evaluation of prognostic factors. J Natl Cancer Inst 1994;86:829-35.
40. Brier GW. Verification of weather forecasts expressed in terms of probability. Monthly Weather Rev 1905;78:1-3.
41. Graf E, Schmoor C, Sauerbrei W, Schumacher, M. Assessment and comparison of prognostic classification schemes for survival data. Stat Med 1999;18:2529-45.
42. Schemper M, Henderson, R. Predictive accuracy and explained variation in Cox regression. Biometrics 2000;56:249-55.
43. Henderson R, Jones M, Stare J. Accuracy of point predictions in survival analysis. Stat Med 2001;20:3083-96.