International Journal of Endocrinology and Metabolism

Published by: Kowsar

Survival Regression Modeling Strategies in CVD Prediction

Mahnaz Barkhordari 1 , Mojgan Padyab 2 , Mahsa Sardarinia 3 , Farzad Hadaegh 3 , Fereidoun Azizi 4 and Mohammadreza Bozorgmanesh 3 , *
Authors Information
1 Department of Mathematics, Bandar Abbas Branch, Islamic Azad University, Bandar Abbas, IR Iran
2 Centre for Population Studies, Ageing and Living Conditions, Umea University, Sweden
3 Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IR Iran
4 Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IR Iran
Article information
  • International Journal of Endocrinology and Metabolism: April 01, 2016, 14 (2); e32156
  • Published Online: March 23, 2016
  • Article Type: Research Article
  • Received: August 8, 2015
  • Revised: November 27, 2015
  • Accepted: January 2, 2016
  • DOI: 10.5812/ijem.32156

To Cite: Barkhordari M, Padyab M, Sardarinia M, Hadaegh F, Azizi F, et al. Survival Regression Modeling Strategies in CVD Prediction, Int J Endocrinol Metab. 2016 ;14(2):e32156. doi: 10.5812/ijem.32156.

Abstract
Copyright © 2016, Research Institute For Endocrine Sciences and Iran Endocrine Society. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.
1. Background
2. Objectives
3. Materials and Methods
4. Results
5. Discussion
Acknowledgements
Footnotes
References
  • 1. Pencina MJ, D'Agostino RB, Vasan RS. Statistical methods for assessment of added usefulness of new biomarkers. Clin Chem Lab Med. 2010; 48(12): 1703-11[DOI][PubMed]
  • 2. Pencina MJ, D'Agostino RS, D'Agostino RJ, 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]
  • 3. Pepe MS. Problems with risk reclassification methods for evaluating prediction models. Am J Epidemiol. 2011; 173(11): 1327-35[DOI][PubMed]
  • 4. Yadegari H, Bozorgmanesh M, Hadaegh F, Azizi F. Non-linear contribution of glucose measures to cardiovascular diseases and mortality: reclassifying the Framingham's risk categories: a decade follow-up from the Tehran lipid and glucose study. Int J Cardiol. 2013; 167(4): 1486-94[DOI][PubMed]
  • 5. Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation. 2007; 115(7): 928-35[DOI][PubMed]
  • 6. Cook NR. Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve. Clin Chem. 2008; 54(1): 17-23[DOI][PubMed]
  • 7. Cook NR, Buring JE, Ridker PM. The effect of including C-reactive protein in cardiovascular risk prediction models for women. Ann Intern Med. 2006; 145(1): 21-9[PubMed]
  • 8. Cook NR, Paynter NP. Performance of reclassification statistics in comparing risk prediction models. Biometrical Journal. 2011; 53(2): 237-58
  • 9. Steyerberg EW. Clinical prediction models: a practical approach to development, validation, and updatin. 2009; [DOI]
  • 10. Wilson PWF, D'Agostino RB, Bhatt DL, Eagle K, Pencina MJ, Smith SC, et al. An international model to predict recurrent cardiovascular disease. Am J Med. 2012; 125(7): 695-703. e1
  • 11. Cooney MT, Dudina AL, Graham IM. Value and limitations of existing scores for the assessment of cardiovascular risk: a review for clinicians. J Am Coll Cardiol. 2009; 54(14): 1209-27[DOI][PubMed]
  • 12. D’Agostino RB, Nam BH. Evaluation of the performance of survival analysis models: discrimination and calibration measures. Handbook of statistics. 2004; 23: 1-25
  • 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. Harrell FE. Regression modeling strategies. 2001; [DOI]
  • 15. Harrell FE, Lee KL, Mark DB. Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors. Statistics in Medicine. 1996; 15(4): 361-87[DOI]
  • 16. Polak JF, Meisner A, Pencina MJ, Wolf PA, D'Agostino RB. Variations in common carotid artery intima-media thickness during the cardiac cycle: implications for cardiovascular risk assessment. J Am Soc Echocardiogr. 2012; 25(9): 1023-8[DOI][PubMed]
  • 17. Chambless LE, Cummiskey CP, Cui G. Several methods to assess improvement in risk prediction models: extension to survival analysis. Stat Med. 2011; 30(1): 22-38[DOI][PubMed]
  • 18. Pencina MJ, D'Agostino RS, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011; 30(1): 11-21[DOI][PubMed]
  • 19. Azizi F, Ghanbarian A, Momenan AA, Hadaegh F, Mirmiran P, Hedayati M, et al. Prevention of non-communicable disease in a population in nutrition transition: Tehran Lipid and Glucose Study phase II. Trials. 2009; 10: 5[DOI][PubMed]
  • 20. Freedman DS, Kahn HS, Mei Z, Grummer-Strawn LM, Dietz WH, Srinivasan SR, et al. Relation of body mass index and waist-to-height ratio to cardiovascular disease risk factors in children and adolescents: the Bogalusa Heart Study. Am J Clin Nutr. 2007; 86(1): 33-40[PubMed]
  • 21. Hadaegh F, Harati H, Ghanbarian A, Azizi F. Association of total cholesterol versus other serum lipid parameters with the short-term prediction of cardiovascular outcomes: Tehran Lipid and Glucose Study. Eur J Cardiovasc Prev Rehabil. 2006; 13(4): 571-7[DOI][PubMed]
  • 22. Gibbons RJ, Abrams J, Chatterjee K, Daley J, Deedwania PC, Douglas JS, et al. ACC/AHA 2002 guideline update for the management of patients with chronic stable angina--summary article: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee on the Management of Patients With Chronic Stable Angina). Circulation. 2003; 107(1): 149-58[PubMed]
  • 23. Braunwald E, Antman EM, Beasley JW, Califf RM, Cheitlin MD, Hochman JS, et al. ACC/AHA guideline update for the management of patients with unstable angina and non-ST-segment elevation myocardial infarction--2002: summary article: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee on the Management of Patients With Unstable Angina). Circulation. 2002; 106(14): 1893-900[PubMed]
  • 24. Bozorgmanesh M. New prediction rule for incident hypertension: Atherosclerosis Risk in Communities (ARIC) Study/Cardiovascular Health Study (CHS). J Clin Hypertens (Greenwich). 2011; 13(10): 780[DOI][PubMed]
  • 25. Bozorgmanesh M, Hadaegh F, Azizi F. A simple clinical model predicted diabetes progression among prediabetic individuals. Diabetes Res Clin Pract. 2012; 97(2)-6
  • 26. Bozorgmanesh M, Hadaegh F, Azizi F. Diabetes prediction, lipid accumulation product, and adiposity measures; 6-year follow-up: Tehran lipid and glucose study. Lipids Health Dis. 2010; 9: 45[DOI][PubMed]
  • 27. Bozorgmanesh M, Hadaegh F, Azizi F. Predictive performances of lipid accumulation product vs. adiposity measures for cardiovascular diseases and all-cause mortality, 8.6-year follow-up: Tehran lipid and glucose study. Lipids Health Dis. 2010; 9(1): 100[DOI]
  • 28. Bozorgmanesh M, Hadaegh F, Azizi F. Predictive performance of the visceral adiposity index for a visceral adiposity-related risk: type 2 diabetes. Lipids Health Dis. 2011; 10: 88[DOI][PubMed]
  • 29. Bozorgmanesh M, Hadaegh F, Azizi F. Predictive accuracy of the ‘Framingham’s general CVD algorithm’in a Middle Eastern population: Tehran Lipid and Glucose Study. Int J Clin Pract. 2011; 65(3): 264-73
  • 30. Bozorgmanesh M, Hadaegh F, Azizi F. Beta-cell age calculator, a translational yardstick to communicate diabetes risk with patients: tehran lipid and glucose study. ISRN Family Med. 2013; 2013: 541091[DOI][PubMed]
  • 31. Bozorgmanesh M, Hadaegh F, Ghaffari S, Harati H, Azizi F. A simple risk score effectively predicted type 2 diabetes in Iranian adult population: population-based cohort study. Eur J Public Health. 2011; 21(5): 554-9
  • 32. Bozorgmanesh M, Hadaegh F, Mehrabi Y, Azizi F. A point-score system superior to blood pressure measures alone for predicting incident hypertension: Tehran Lipid and Glucose Study. Journal of hypertension. 2011; 29(8): 1486-93
  • 33. Mohammadreza B, Farzad H, Davoud K, Fereidoun Prof AF. Prognostic significance of the complex "Visceral Adiposity Index" vs. simple anthropometric measures: Tehran lipid and glucose study. Cardiovasc Diabetol. 2012; 11: 20[DOI][PubMed]
  • 34. Bozorgmanesh M, Hadaegh F, Zabetian A, Azizi F. San Antonio heart study diabetes prediction model applicable to a Middle Eastern population? Tehran glucose and lipid study. Int J Public Health. 2010; 55(4): 315-23[DOI][PubMed]
  • 35. Pencina MJ, D'Agostino RB, Pencina KM, Janssens ACJW, Greenland P. Interpreting Incremental Value of Markers Added to Risk Prediction Models. American Journal of Epidemiology. 2012; 176(6): 473-81[DOI]
Creative Commons License Except where otherwise noted, this work is licensed under Creative Commons Attribution Non Commercial 4.0 International License .

Search Relations:

Author(s):

Article(s):

Create Citiation Alert
via Google Reader

Readers' Comments