Assessing the incremental predictive performance of novel biomarkers over standard predictors

Statistics in Medicine

Volumn 33, Issue 15, 2014, Pages 2577-2584

  Xanthakis, Vanessa ^a,b,c   Sullivan, Lisa M ^b   Vasan, Ramachandran S ^a,c   Benjamin, Emelia J ^a,c   Massaro, Joseph M ^a,b,d   D'Agostino, Ralph B ^a,d   Pencina, Michael J ^a,b,d  

^a FRAMINGHAM HEART STUDY   (United States)

^b BOSTON UNIVERSITY SCHOOL OF PUBLIC HEALTH   (United States)

^c BOSTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d USA   (United States)

Author keywords

Biomarkers; Model discrimination; Risk model; Risk prediction

Indexed keywords

BRAIN NATRIURETIC PEPTIDE; C REACTIVE PROTEIN; BIOLOGICAL MARKER;

ADULT; ARTICLE; BODY MASS; CLINICAL ASSESSMENT; CLINICAL EVALUATION; CONGESTIVE HEART FAILURE; DISEASE ASSOCIATION; FEMALE; FRAMINGHAM RISK SCORE; HEART ATRIUM FIBRILLATION; HEART MURMUR; HUMAN; HYPERTENSION; MAJOR CLINICAL STUDY; MALE; PREDICTIVE VALUE; RISK FACTOR; SIMULATION; SYSTOLIC BLOOD PRESSURE; ATRIAL FIBRILLATION; BIOLOGICAL MODEL; PROCEDURES; RISK ASSESSMENT; STATISTICAL MODEL;

ATRIAL FIBRILLATION; BIOLOGICAL MARKERS; C-REACTIVE PROTEIN; FEMALE; HUMANS; MALE; MODELS, CARDIOVASCULAR; MODELS, STATISTICAL; NATRIURETIC PEPTIDE, BRAIN; PREDICTIVE VALUE OF TESTS; RISK ASSESSMENT;

EID: 84901932563     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.6165     Document Type: Article

References (17)

1. Pencina MJ, D'Agostino RB, Vasan RS. Statistical methods for assessment of added usefulness of new biomarkers. Clinical Chemistry and Laboratory Medicine 2010; 48(12): 1703-1711.
2. Hlatky MA, Greenland P, Arnett DK, Ballantyne CM, Criqui MH, Elkind MSV, Go AS, Harrell FE, Hong Y, Howard BV, Howard VJ, Hsue PY, Kramer CM, McConnell JP, Normand SL, O'Donnell CJ, Smith SC, Wilson PWF, on behalf of the American Heart Association Expert Panel on Subclinical Atherosclerotic Diseases and Emerging Risk Factors and the Stroke Council. Criteria for evaluation of novel markers of cardiovascular risk. Circulation 2009; 119(17): 2408-2416.
3. 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. Statistics in Medicine 2008; 27(2): 157-172.
4. Pencina MJ, D'Agostino RB. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Statistics in Medicine 2004; 23(13): 2109-2123.
5. 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. American Journal of Epidemiology 2004; 159(9): 882-890.
6. Cook NR, Buring JE, Ridker PM. The effect of including C-reactive protein in cardiovascular risk prediction models for women. Annals of Internal Medicine 2006; 145(1): 21-29.
7. Ingelsson E, Schaefer EJ, Contois JH, McNamara JR, Sullivan L, Keyes MJ, Pencina MJ, Schoonmaker C, Wilson PWF, D ÇÖAgostino RB, Vasan RS. Clinical utility of different lipid measures for prediction of coronary heart disease in men and women. JAMA: The Journal of the American Medical Association 2007; 298(7): 776-785.
8. Kim HC, Greenland P, Rossouw JE, Manson JE, Cochrane BB, Lasser NL, Limacher MC, Lloyd-Jones DM, Margolis KL, Robinson JG. Multimarker prediction of coronary heart disease risk: the Women's Health Initiative. Journal of the American College of Cardiology 2010; 55(19): 2080-2091.
9. Ridker PM, Buring JE, Rifai N, Cook NR. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. Journal of the American Medical Association 2007; 297(6): 611-619.
10. Moons KGM, Kengne AP, Grobbee DE, Royston P, Vergouwe Y, Altman DG, Woodward M. Risk prediction models: II. External validation, model updating, and impact assessment. Heart 2012; 98(9): 691-698.
11. Pencina MJ, D'Agostino RB, Sr., Steyerberg E. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Statistics in Medicine 2011; 30(1): 11-21.
12. Dawber TR, Meadors GF, Moore FE. Epidemiologic approaches to heart disease: the Framingham Study. American Journal of Public Health 1951; 41: 279-286.
13. Kannel WB, Feinleib M, McNamara PM, Garrison RJ, Castelli WP. An investigation of coronary heart disease in families. The Framingham Offspring Study. American Journal of Epidemiology 1979; 110(3): 281-290.
14. Schnabel RB, Sullivan LM, Levy D, Pencina MJ, Massaro JM, D'Agostino RB, Newton-Cheh C, Yamamoto JF, Magnani JW, Tadros TM, Kannel WB, Wang TJ, Ellinor PT, Wolf PA, Vasan RS, Benjamin EJ. Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study. The Lancet 2009; 373(9665): 739-745.
15. Steyerberg EW, Borsboom GJ, van Houwelingen HC, Eijkemans MJ, Habbema JD. Validation and updating of predictive logistic regression models: a study on sample size and shrinkage. Statistics in Medicine 2004; 23(16): 2567-2586.
16. Tzoulaki I, Liberopoulos G, Ioannidis JP. Assessment of claims of improved prediction beyond the Framingham risk score. Journal of the American Medical Association 2009; 302(21): 2345-2352.
17. Pepe MS, Kerr KF, Longton G, Wang Z. Testing for improvement in prediction model performance. Statistics in Medicine 2013; 32(9): 1467-1482.