Traditional Statistical Methods for Evaluating Prediction Models Are Uninformative as to Clinical Value: Towards a Decision Analytic Framework

Seminars in Oncology

Volumn 37, Issue 1, 2010, Pages 31-38

  Vickers, Andrew J ^a   Cronin, Angel M ^a  

^a MEMORIAL SLOAN KETTERING CANCER CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BREAST CANCER; CALIBRATION; CANCER SURVIVAL; CLINICAL DECISION MAKING; CONCEPTUAL FRAMEWORK; DIAGNOSTIC VALUE; MALIGNANT NEOPLASTIC DISEASE; OUTCOMES RESEARCH; PREDICTION; PRIORITY JOURNAL; PROGNOSIS; STATISTICAL MODEL;

AREA UNDER CURVE; CALIBRATION; DECISION SUPPORT TECHNIQUES; HUMANS; MODELS, STATISTICAL; NEOPLASMS; RISK;

EID: 76749097988     PISSN: 00937754     EISSN: None     Source Type: Journal    
DOI: 10.1053/j.seminoncol.2009.12.004     Document Type: Article

References (33)

1. Murphy N.C., Biankin A.V., Millar E.K., et al. Loss of STARD10 expression identifies a group of poor prognosis breast cancers independent of HER2/Neu and triple negative status. Int J Cancer (August 12, 2009) Epub ahead of print
2. Korse C.M., Taal B.G., de Groot C.A., Bakker R.H., and Bonfrer J.M. Chromogranin-A and N-terminal pro-brain natriuretic peptide: an excellent pair of biomarkers for diagnostics in patients with neuroendocrine tumor. J Clin Oncol. 27 (2009) 4293-4299
3. Garcia-Albeniz X., and Gallego R. Prognostic role of plasma insulin-like growth factor (IGF) and IGF-binding protein 3 in metastatic colorectal cancer. Clin Cancer Res. 15 (2009) 5288
4. Stinchcombe T.E., Hodgson L., Herndon II J.E., et al. Treatment outcomes of different prognostic groups of patients on Cancer and Leukemia Group B trial 39801: induction chemotherapy followed by chemoradiotherapy compared with chemoradiotherapy alone for unresectable stage III non-small cell lung cancer. J Thorac Oncol. 4 (2009) 1117-1125
5. Warren M., Venner P.M., North S., et al. A population-based study examining the effect of tyrosine kinase inhibitors on survival in metastatic renal cell carcinoma in Alberta and the role of nephrectomy prior to treatment. Can Urol Assoc J. 3 (2009) 281-289
6. Cooperberg M.R., Hinotsu S., Namiki M., et al. Risk assessment among prostate cancer patients receiving primary androgen deprivation therapy. J Clin Oncol. 27 (2009) 4306-4313
7. Monzon J.G., Cremin C., Armstrong L., et al. Validation of predictive models for germline mutations in DNA mismatch repair genes in colorectal cancer. Int J Cancer 27 (2009) 4555-4562
8. Federico M., Bellei M., Marcheselli L., et al. Follicular Lymphoma International Prognostic Index 2: a new prognostic index for follicular lymphoma developed by the International Follicular Lymphoma Prognostic Factor Project. J Clin Oncol. 27 (2009) 4555-4562
9. Shariat S.F., Karakiewicz P.I., Roehrborn C.G., et al. An updated catalog of prostate cancer predictive tools. Cancer 113 (2008) 3075-3099
10. Thompson I.M., Ankerst D.P., Chi C., et al. Assessing prostate cancer risk: results from the Prostate Cancer Prevention Trial. J Natl Cancer Inst. 98 (2006) 529-534
11. Olivotto I.A., Bajdik C.D., Ravdin P.M., et al. Population-based validation of the prognostic model ADJUVANT! for early breast cancer. J Clin Oncol. 23 (2005) 2716-2725
12. Marchionni L., Wilson R.F., Wolff A.C., et al. Systematic review: gene expression profiling assays in early-stage breast cancer. Ann Intern Med. 148 (2008) 358-369
13. Sheridan S., Pignone M., and Mulrow C. Framingham-based tools to calculate the global risk of coronary heart disease: a systematic review of tools for clinicians. J Gen Intern Med. 18 (2003) 1039-1052
14. Knaus W.A., Draper E.A., Wagner D.P., and Zimmerman J.E. APACHE II: a severity of disease classification system. Crit Care Med. 13 (1985) 818-829
15. Parody R., Martino R., Sanchez F., Subira M., Hidalgo A., and Sierra J. Predicting survival in adults with invasive aspergillosis during therapy for hematological malignancies or after hematopoietic stem cell transplantation: single-center analysis and validation of the Seattle, French, and Strasbourg prognostic indexes. Am J Hematol. 84 (2009) 571-578
16. 2max during cycle ergometry based on submaximal ventilatory indicators. J Strength Cond Res. 23 (2009) 1745-1751
17. Schmid-Mohler G., Thut M.P., Wuthrich R.P., Denhaerynck K., and De Geest S. Non-adherence to immunosuppressive medication in renal transplant recipients within the scope of the integrative model of behavioral prediction: a cross-sectional study. Clin Transplant. (Aug 11, 2009) Epub ahead of print
18. Goldraich L., Beck-da-Silva L., and Clausell N. Are scores useful in advanced heart failure?. Expert Rev Cardiovasc Ther. 7 (2009) 985-997
19. Koenig W., Vossen C.Y., Mallat Z., Brenner H., Benessiano J., and Rothenbacher D. Association between type II secretory phospholipase A2 plasma concentrations and activity and cardiovascular events in patients with coronary heart disease. Eur Heart J. 30 (2009) 2742-2748
20. Verhoeven C.J., Oudenaarden A., Hermus M.A., Porath M.M., Oei S.G., and Mol B.W. Validation of models that predict cesarean section after induction of labor. Ultrasound Obstet Gynecol 34 (2009) 316-321
21. Law L.W., Leung T.Y., Sahota D.S., Chan L.W., Fung T.Y., and Lau T.K. Which ultrasound or biochemical markers are independent predictors of small-for-gestational age?. Ultrasound Obstet Gynecol 34 (2009) 283-287
22. Massicotte L., Capitanio U., Beaulieu D., Roy J.D., Roy A., and Karakiewicz P.I. Independent validation of a model predicting the need for packed red blood cell transfusion at liver transplantation. Transplantation 88 (2009) 386-391
23. Kosmider O., Gelsi-Boyer V., Cheok M., et al. TET2 mutation is an independent favorable prognostic factor in myelodysplastic syndromes (MDS). Blood 114 (2009) 3285-3291
24. Lin C.L., Lin P.H., Chou L.W., et al. Model-based prediction of length of stay for rehabilitating stroke patients. J Formos Med Assoc. 108 (2009) 653-662
25. Kattan M.W. Judging new markers by their ability to improve predictive accuracy. J Natl Cancer Inst. 95 (2003) 634-635
26. Kattan M.W., Eastham J.A., Stapleton A.M., Wheeler T.M., and Scardino P.T. A preoperative nomogram for disease recurrence following radical prostatectomy for prostate cancer. J Natl Cancer Inst. 90 (1998) 766-771
27. Kuijpers T., van der Heijden G.J., Vergouwe Y., et al. Good generalizability of a prediction rule for prediction of persistent shoulder pain in the short term. J Clin Epidemiol. 60 (2007) 947-953
28. Grover S.A., Hemmelgarn B., Joseph L., Milot A., and Tremblay G. The role of global risk assessment in hypertension therapy. Can J Cardiol. 22 (2006) 606-613
29. Zorn K.C., Capitanio U., Jeldres C., et al. Multi-institutional external validation of seminal vesicle invasion nomograms: head-to-head comparison of Gallina nomogram versus 2007 Partin tables. Int J Radiat Oncol Biol Phys. 73 (2009) 1461-1467
30. Tice J.A., Cummings S.R., Smith-Bindman R., Ichikawa L., Barlow W.E., and Kerlikowske K. Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model. Ann Intern Med. 148 (2008) 337-347
31. Janes H., Pepe M.S., and Gu W. Assessing the value of risk predictions by using risk stratification tables. Ann Intern Med. 149 (2008) 751-760
32. Vickers A.J., and Elkin E.B. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 26 (2006) 565-574
33. Vickers A.J., Cronin A.M., Elkin E.B., and Gonen M. Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inform Decis Mak. 8 (2008) 53