Iranian Red Crescent Medical Journal

Published by: Kowsar

Assessment of the Importance of a New Risk Factor in Prediction Models

Mohammad Reza Baneshi 1 , Ehsan Mosa Farkhani 2 and Saiedeh Haji-Maghsoudi 3 , *
Authors Information
1 Research Center for Modeling in Health, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, IR Iran
2 Department of Epidemiology, University of Tehran, Tehran, IR Iran
3 Department of Biostatistics & Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, IR Iran
Article information
  • Iranian Red Crescent Medical Journal: February 01, 2016, 18 (2); e20949
  • Published Online: February 6, 2015
  • Article Type: Research Article
  • Received: June 15, 2014
  • Revised: September 3, 2014
  • Accepted: September 28, 2014
  • DOI: 10.5812/ircmj.20949

To Cite: Baneshi M R, Mosa Farkhani E, Haji-Maghsoudi S. Assessment of the Importance of a New Risk Factor in Prediction Models, Iran Red Crescent Med J. 2016 ; 18(2):e20949. doi: 10.5812/ircmj.20949.

Copyright © 2015, Iranian Red Crescent Medical Journal. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.
1. Background
2. Objectives
3. Patients and Methods
4. Results
5. Discussion
  • 1. Pencina MJ, D'Agostino RB, D'Agostino RB, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008; 27(2): 157-72[DOI][PubMed]
  • 2. Moons KG, Kengne AP, Woodward M, Royston P, Vergouwe Y, Altman DG, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart. 2012; 98(9): 683-90[DOI][PubMed]
  • 3. Pencina MJ, D'Agostino RB, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011; 30(1): 11-21[DOI][PubMed]
  • 4. Hoyt WT, Leierer S, Millington MJ. Analysis and Interpretation of Findings Using Multiple Regression Techniques. Rehabil Couns Bull. 2006; 49(4): 223-33[DOI]
  • 5. Mac Nally R. Multiple regression and inference in ecology and conservation biology: further comments on identifying important predictor variables. Biodivers Conserv. 2002; 11(8): 1397-401[DOI]
  • 6. Nathans LL, Oswald FL, Nimon K. Interpreting multiple linear regression: A guidebook of variable importance. 2012; 17(9)
  • 7. Sauerbrei W, Royston P, Look M. A new proposal for multivariable modelling of time-varying effects in survival data based on fractional polynomial time-transformation. Biom J. 2007; 49(3): 453-73[DOI][PubMed]
  • 8. Sauerbrei W, Royston P, Binder H. Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Stat Med. 2007; 26(30): 5512-28[DOI][PubMed]
  • 9. Levy AR, Tamblyn RM, Fitchett D, McLeod PJ, Hanley JA. Coding accuracy of hospital discharge data for elderly survivors of myocardial infarction. Can J Cardiol. 1999; 15(11): 1277-82[PubMed]
  • 10. Farkhani EM, Baneshi MR, Zolala F. Survival Rate And Its Related Factors In Patients With Acute Myocardial Infarction. Med J Mashhad Univ Med Sci. 2014; 57(4): 636-46
  • 11. Justice AC, Covinsky KE, Berlin JA. Assessing the generalizability of prognostic information. Ann Intern Med. 1999; 130(6): 515-24[PubMed]
  • 12. Harrell FE, Lee KL, Mark DB. Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors. Stat Med. 1996; 15(4): 361-87[DOI]
  • 13. Pencina MJ, D'Agostino RB. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med. 2004; 23(13): 2109-23[DOI][PubMed]
  • 14. Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation. 2007; 115(7): 928-35[DOI][PubMed]
  • 15. Harrell FE. Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. 2001;
  • 16. Cook NR. Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve. Clin Chem. 2008; 54(1): 17-23[DOI][PubMed]
  • 17. Greenland P, O'Malley PG. When is a new prediction marker useful? A consideration of lipoprotein-associated phospholipase A2 and C-reactive protein for stroke risk. Arch Intern Med. 2005; 165(21): 2454-6[DOI][PubMed]
  • 18. 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. Am J Epidemiol. 2004; 159(9): 882-90[PubMed]
  • 19. Ware JH. The limitations of risk factors as prognostic tools. N Engl J Med. 2006; 355(25): 2615-7[DOI][PubMed]
  • 20. Wang TJ, Gona P, Larson MG, Tofler GH, Levy D, Newton-Cheh C, et al. Multiple biomarkers for the prediction of first major cardiovascular events and death. N Engl J Med. 2006; 355(25): 2631-9[DOI][PubMed]
  • 21. Uno H, Tian L, Cai T, Kohane IS, Wei LJ. Comparing risk scoring systems beyond the ROC paradigm in survival analysis. Harvard Univ Biostatistics Work Pap Ser. 2009;
  • 22. Meigs JB, Shrader P, Sullivan LM, McAteer JB, Fox CS, Dupuis J, et al. Genotype score in addition to common risk factors for prediction of type 2 diabetes. N Engl J Med. 2008; 359(21): 2208-19[DOI][PubMed]
Creative Commons License Except where otherwise noted, this work is licensed under Creative Commons Attribution Non Commercial 4.0 International License .

Search Relations:



Create Citiation Alert
via Google Reader

Readers' Comments