Tailoring the implementation of new biomarkers based on their added predictive value in subgroups of individuals

PLoS ONE

Volumn 10, Issue 1, 2015, Pages

  Van Giessen, A ^a   Moons, K G M ^a   De Wit, G A ^a,b   Verschuren, W M M ^a,b   Boer, J M A ^b   Koffijberg, H ^a  

^a UNIVERSITY MEDICAL CENTER UTRECHT   (Netherlands)

^b NATIONAL INSTITUTE FOR PUBLIC HEALTH AND THE ENVIRONMENT   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; ARTICLE; ASSESSMENT OF HUMANS; CARDIOVASCULAR DISEASE ASSESSMENT; CARDIOVASCULAR RISK; CLUSTER ANALYSIS; DUTCHMAN; FEMALE; FRAMINGHAM RISK SCORE; HIGH RISK POPULATION; HUMAN; INTERMETHOD COMPARISON; LOW RISK POPULATION; MALE; NET RECLASSIFICATION IMPROVEMENT; PREDICTIVE VALUE; PREDICTOR VARIABLE; RISK ASSESSMENT; RISK BENEFIT ANALYSIS; SYSTEMATIC CORONARY RISK EVALUATION; CARDIOVASCULAR DISEASES; RISK FACTOR;

BIOLOGICAL MARKER;

BIOMARKERS; CARDIOVASCULAR DISEASES; CLUSTER ANALYSIS; HUMANS; RISK ASSESSMENT; RISK FACTORS;

EID: 84921956350     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0114020     Document Type: Article

References (50)

1. Ankerst DP, Koniarski T, Liang Y, Leach RJ, Feng Z, et al. (2012) Updating risk prediction tools: a case study in prostate cancer. Biom J 54: 127-142. doi: 10.1002/bimj.201100062
2. Kavousi M, Elias-Smale S, Rutten JH, Leening MJ, Vliegenthart R, et al. (2012) Evaluation of newer risk markers for coronary heart disease risk classification: a cohort study. Ann Intern Med 156: 438-444. doi: 10.7326/0003-4819-156-6-201203200-00006 PMID: 22431676
3. Koller MT, Leening MJ, Wolbers M, Steyerberg EW, Hunink MG, et al. (2012) Development and validation of a coronary risk prediction model for older U.S. and European persons in the cardiovascular health study and the Rotterdam Study. Ann Intern Med 157: 389-397. doi: 10.7326/0003-4819-157-6-201209180-00002 PMID: 22986376
4. Hlatky MA, Greenland P, Arnett DK, Ballantyne CM, Criqui MH, et al. (2009) Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association. Circulation 119: 2408-2416. doi: 10.1161/CIRCULATIONAHA.109.192278 PMID: 19364974
5. Wallace E, Smith SM, Perera-Salazar R, Vaucher P, McCowan C, et al. (2011) Framework for the impact analysis and implementation of Clinical Prediction Rules (CPRs). BMC Med Inform Decis Mak 11: 62. doi: 10.1186/1472-6947-11-62 PMID: 21999201
6. Moons KG, Kengne AP, Grobbee DE, Royston P, Vergouwe Y, et al. (2012) Risk prediction models: II. External validation, model updating, and impact assessment. Heart 98: 691-698.
7. Moons KG, Kengne AP, Woodward M, Royston P, Vergouwe Y, et al. (2012) Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart 98: 683-690.
8. SteyerbergEW, Pencina MJ, Lingsma HF, Kattan MW, Vickers AJ, et al. (2012) Assessing the incremental value of diagnostic and prognostic markers: a review and illustration. Eur J Clin Invest 42: 216-228. doi: 10.1111/j.1365-2362.2011.02562.x PMID: 21726217
9. Pencina MJ, D'Agostino RB, Pencina KM, Janssens AC, Greenland P. (2012) Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol 176: 473-481. doi: 10.1093/aje/ kws207 PMID: 22875755
10. Pencina MJ, D'Agostino RB, Sr. D'Agostino RB, Jr. Vasan RS. (2008) Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 27: 157-172. doi: 10.1002/sim.2929 PMID: 17569110
11. Tian L, Cai T, Wei LJ. (2009) Identifying subjects who benefit from additional information for better prediction of the outcome variables. Biometrics 65: 894-902. doi: 10.1111/j.1541-0420.2008.01125.x PMID: 18945268
12. Leening MJ, Vedder MM, Witteman JC, Pencina MJ, Steyerberg EW. (2014) Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician's guide. Ann Intern Med 160: 122-131. doi: 10.7326/M13-1522 PMID: 24592497
13. Vickers AJ, Pepe M. (2014) Does the net reclassification improvement help us evaluate models and markers? Ann Intern Med 160: 136-137. doi: 10.7326/M13-2841 PMID: 24592500
14. Cook NR. (2012) Clinically relevant measures of fit? A note of caution. Am J Epidemiol 176: 488-491. doi: 10.1093/aje/kws208 PMID: 22875759
15. Anderson KM, Odell PM, Wilson PW, Kannel WB. (1991) Cardiovascular disease risk profiles. Am Heart J 121: 293-298. doi: 10.1016/0002-8703(91)90861-B PMID: 1985385
16. Assmann G, Cullen P, Schulte H. (2002) Simple scoring scheme for calculating the risk of acute coronary events based on the 10-year follow-up of the prospective cardiovascular Munster (PROCAM) study. Circulation 105: 310-315. doi: 10.1161/hc0302.102575 PMID: 11804985
17. Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, May M, et al. (2007) Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ 335: 136. doi: 10.1136/bmj.39261.471806.55 PMID: 17615182
18. Conroy RM, Pyorala K, Fitzgerald AP, Sans S, Menotti A, et al. (2003) Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J 24: 987-1003. doi: 10.1016/ S0195-668X(03)00114-3 PMID: 12788299
19. Siontis GC, Tzoulaki I, Siontis KC, Ioannidis JP. (2012) Comparisons of established risk prediction models for cardiovascular disease: systematic review. BMJ 344: e3318. doi: 10.1136/bmj.e3318 PMID: 22628003
20. Blokstra A, Smit HA, Bueno de Mesquita HB, Seidell JC, Verschuren WMM. Monitoring van Risicofactoren en Gezondheid in Nederland (MORGEN-project), 1993-1997. 2005. RIVM, Bilthoven.
21. th Edition ed. West Sussex, United Kingdom: John Wiley & Sons, Ltd.
22. Aldenderfer MS, Blashfield RK (1984) Cluster analysis. Beverly Hills, CA: Sage Publications, Inc.
23. Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis. New York: John Wiley & Sons, Inc.
24. Everitt BS. (1979) Unresolved problems in cluster analysis. Biometrics 35: 169-181. doi: 10.2307/ 2529943
25. Halkidi M, Batistakis Y, Vazigiannis M. (2001) On clustering validation techniques. Journal of Intelligent Information Systems 17: 107-145. doi: 10.1023/A:1012801612483
26. Levy D, Labib SB, Anderson KM, Christiansen JC, Kannel WB, et al. (1990) Determinants of sensitivity and specificity of electrocardiographic criteria for left ventricular hypertrophy. Circulation 81: 815-820. doi: 10.1161/01.CIR.81.3.815 PMID: 2137733
27. Leening MJG, Steyerberg EW, Van Calster B, D'Agostino RB, Pencina MJ. (2014) Net reclassification improvement and integrated discrimination improvement require calibrated models: relevance from a marker and model perspective. Statist Med 33: 3415-3418. doi: 10.1002/sim.6133
28. van Houwelingen HC. (2000) Validation, calibration, revision and combination of prognostic survival models. Stat Med 19: 3401-3415. doi: 10.1002/1097-0258(20001230)19:24%3C3401::AID-SIM554% 3E3.0.CO;2-2 PMID: 11122504
29. Perk J, De BG, Gohlke H, Graham I, Reiner Z, et al. (2012) European guidelines on cardiovascular disease prevention in clinical practice (version 2012): the fifth joint task force of the European society of cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of nine societies and by invited experts). Int J BehavMed 19: 403-488. PMID: 23093473
30. van Dis I, Kromhout D, Geleijnse JM, Boer JM, Verschuren WM. (2010) Evaluation of cardiovascular risk predicted by different SCORE equations: the Netherlands as an example. Eur J Cardiovasc Prev Rehabil 17: 244-249. doi: 10.1097/HJR.0b013e328337cca2 PMID: 20195155
31. Pencina MJ, D'Agostino RB, Sr. Steyerberg EW. (2011) Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med 30: 11-21. doi: 10.1002/sim.4085 PMID: 21204120
32. IBM SPSS Statistics Base 20. 2011. Chicago, IL, SPSS.
33. Bacher J, Wenzig K, Vogler M. (2004) SPSS TwoStep Clustering -A First Evaluation. Proceedings of the RC33 Sixth International Conference on Social Science Methodology.
34. Norusis MJ, SPSS Inc. (2012) Cluster Analysis. In: Pearson, editor. IBM SPSS Statistics 19 Statistical Procedures Companion. pp. 361-391.
35. Formann AK (1984) Die Latent-Class-Analyse. Einführung in Theorie und Anwendung. Weinheim: Beltz.
36. Dolnicar S, Leisch F. (2010) Evaluation of structure and reproducibility of cluster solutions using bootstrap. Marketing Letters 21: 83-101. doi: 10.1007/s11002-009-9083-4
37. Jain AK, Moreau JV. (1987) Bootstrap technique in cluster analysis. Pattern Recognition 20: 547-568. doi: 10.1016/0031-3203(87)90081-1
38. Efron B, Tibshirani R (1993) An Introduction to the Bootstrap. Boca Raton, FL: Chapman & Hall/CRC.
39. Hubert L, Arabie P. (1985) Comparing partitions. Journal of Classification 2: 193-218. doi: 10.1007/ BF01908075
40. R Core Team. R: A Language and Environment for Statistical Computing. 2012. Vienna, Austria, R Foundation for Statistical Computing.
41. Fraley C, Raftery AE. (2002) Model-based Clustering, Discriminant Analysis and Density Estimation. Journal of the American Statistical Association 97: 611-631. doi: 10.1198/016214502760047131
42. Reilly BM, Evans AT. (2006) Translating clinical research into clinical practice: impact of using prediction rules to make decisions. Ann Intern Med 144: 201-209. doi: 10.7326/0003-4819-144-3-200602070-00009 PMID: 16461965
43. Moons KG, Altman DG, Vergouwe Y, Royston P. (2009) Prognosis and prognostic research: application and impact of prognostic models in clinical practice. BMJ 338: b606. doi: 10.1136/bmj.b606 PMID: 19502216
44. den Ruijter HM, Vaartjes I, Sutton-Tyrrell K, Bots ML, Koffijberg H. (2013) Long-term health benefits and costs of measurement of carotid intima-media thickness in prevention of coronary heart disease. J Hypertens 31: 782-790. doi: 10.1097/HJH.0b013e32835e8ee5 PMID: 23442989
45. Yeboah J, McClelland RL, Polonsky TS, Burke GL, Sibley CT, et al. (2012) Comparison of novel risk markers for improvement in cardiovascular risk assessment in intermediate-risk individuals. JAMA 308: 788-795. doi: 10.1001/jama.2012.9624 PMID: 22910756
46. Wierzbicki AS. (2012) New directions in cardiovascular risk assessment: the role of secondary risk stratification markers. Int J Clin Pract 66: 622-630. doi: 10.1111/j.1742-1241.2012.02956.x PMID: 22698414
47. Hung CL, Po HL, Liu CC, Yen CH, Wu YJ, et al. (2013) The utilization of carotid artery imaging beyond metabolic scores and high-sensitivity CRP in screening intermediate-to-high Framingham risk of asymptomatic Taiwanese population. Int J Cardiovasc Imaging 29: 765-775. doi: 10.1007/s10554-012-0146-x PMID: 23124516
48. Herrera F, Carmona CJ, González P, José del Jesus M. (2011) An overview on subgroup discovery: foundations and applications. Knowledge and Information Systems 29: 495-525. doi: 10.1007/ s10115-010-0356-2
49. Gamberger G, Lavrac N. (2002) Expert-guided subgroup discovery: methodology and application. J Artif Int Res 17: 501-527.
50. Jain AK,MurtyMN, Flynn AK. (1999) Data Clustering: A Review. ACMComputing Surveys 31: 264-323. doi: 10.1145/331499.331504