Key concepts and limitations of statistical methods for evaluating biomarkers of kidney disease

Journal of the American Society of Nephrology

Volumn 25, Issue 8, 2014, Pages 1621-1629

  Parikh, Chirag R ^a   Thiessen Philbrook, Heather ^b  

^a YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b UNIVERSITY OF WESTERN ONTARIO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER;

AREA UNDER THE CURVE; DISEASE CLASSIFICATION; EFFECT SIZE; FALSE POSITIVE RESULT; HUMAN; KIDNEY DISEASE; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; REVIEW; RISK FACTOR; STATISTICAL ANALYSIS; DECISION SUPPORT SYSTEM; KIDNEY DISEASES; PREDICTIVE VALUE; REPRODUCIBILITY; RISK;

AREA UNDER CURVE; BIOLOGICAL MARKERS; DECISION SUPPORT TECHNIQUES; HUMANS; KIDNEY DISEASES; ODDS RATIO; PREDICTIVE VALUE OF TESTS; REPRODUCIBILITY OF RESULTS; RISK; ROC CURVE;

EID: 84921635924     PISSN: 10466673     EISSN: 15333450     Source Type: Journal    
DOI: 10.1681/ASN.2013121300     Document Type: Review

References (38)

1. Coca SG, Yalavarthy R, Concato J, Parikh CR: Biomarkers for the diagnosis and risk stratification of acute kidney injury: A systematic review. Kidney Int 73: 1008-1016, 2008
2. Pepe MS, Etzioni R, Feng Z, Potter JD, Thompson ML, ThornquistM,WingetM, Yasui Y: Phases of biomarker development for early detection of cancer. J Natl Cancer Inst 93: 1054-1061, 2001
3. Krzanowski WJ, Hand DJ: ROC Curves for Continuous Data, Boca Raton, FL, Chapman & Hall/CRC, 2009
4. Van Calster B, Van Belle V, Vergouwe Y, Steyerberg EW: Discrimination ability of prediction models for ordinal outcomes: Relationships between existing measures and a new measure. Biom J 54: 674-685, 2012
5. Chambless LE, Diao G: Estimation of timedependent area under the ROC curve for longterm risk prediction. Stat Med 25: 3474-3486, 2006
6. Heagerty PJ, Zheng Y: Survival model predictive accuracy and ROC curves. Biometrics 61: 92-105, 2005
7. Parikh CR,HanG: Variation in performance of kidney injury biomarkers due to cause of acute kidney injury. Am J Kidney Dis 62: 1023-1026, 2013
8. Waikar SS, Betensky RA, Emerson SC, Bonventre JV: Imperfect gold standards for kidney injury biomarker evaluation. J AmSoc Nephrol 23: 13-21, 2012
9. Pepe MS, Janes H, Longton G, LeisenringW, Newcomb P: Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol 159: 882-890, 2004
10. Parikh CR, Coca SG, Thiessen-Philbrook H, Shlipak MG, Koyner JL, Wang Z, Edelstein CL, Devarajan P, Patel UD, Zappitelli M, Krawczeski CD,PassikCS,SwaminathanM,GargAX; TRIBEAKI Consortium: Postoperative biomarkers predict acute kidney injury and poor outcomes after adult cardiac surgery. J Am Soc Nephrol 22: 1748-1757, 2011
11. McGough JJ, Faraone SV: Estimating the size of treatment effects: Moving beyond p values. Psychiatry (Edgmont) 6: 21-29, 2009
12. Janes H, PepeMS: Adjusting for covariates in studies of diagnostic, screening, or prognostic markers: An old concept in a new setting. Am J Epidemiol 168: 89-97, 2008
13. Endre ZH, Pickering JW: Biomarkers and creatinine in AKI: The trough of disillusionment or the slope of enlightenment? Kidney Int 84: 644-647, 2013
14. Murray PT, Mehta RL, Shaw A, Ronco C, Endre Z, Kellum JA, Chawla LS, Cruz D, Ince C, Okusa MD: Potential use of biomarkers in acute kidney injury: Report and summary of recommendations from the 10th Acute Dialysis Quality Initiative consensus conference. Kidney Int 85: 513-521, 2014
15. Huang Y, Pepe MS: Biomarker evaluation and comparison using the controls as a reference population. Biostatistics 10: 228-244, 2009
16. HuangY, PepeMS, FengZ: Logistic regression analysis with standardized markers [published online ahead of print September 1, 2013]. Ann Appl Stat 10.1214/13-AOAS634SUPP
17. Kerr KF, Pepe MS: Joint modeling, covariate adjustment, and interaction: Contrasting notions in risk prediction models and risk prediction performance. Epidemiology 22: 805-812, 2011
18. DeLong ER, DeLongDM,Clarke-PearsonDL: Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 44: 837-845, 1988
19. Begg CB, Gonen M, Seshan VE: Testing the incremental predictive accuracy of new markers. Clin Trials 10: 690-692, 2013
20. Pepe MS, Kerr KF, Longton G, Wang Z: Testing for improvement in predictionmodel performance. Stat Med 32: 1467-1482, 2013
21. Demler OV, Pencina MJ, D'Agostino RB Sr.: Misuse of DeLong test to compare AUCs for nestedmodels. StatMed 31: 2577-2587, 2012
22. Chen HC, Kodell RL, Cheng KF, Chen JJ: Assessment of performance of survival prediction models for cancer prognosis. BMC Med Res Methodol 12: 102, 2012
23. Pencina MJ, D'Agostino RB Sr., D'Agostino RB Jr., Vasan RS: Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond. Stat Med 27: 157-172, 2008
24. Pencina MJ, D'Agostino RB Sr., Steyerberg EW: Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med 30: 11-21, 2011
25. Pencina MJ, D'Agostino RB, Pencina KM, Janssens AC, Greenland P: Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol 176: 473-481, 2012
26. Pencina MJ, D'Agostino RB Sr., Demler OV: Novel metrics for evaluating improvement in discrimination: Net reclassification and integrated discrimination improvement for normal variables and nested models. Stat Med 31: 101-113, 2012
27. Pepe MS: Problems with risk reclassification methods for evaluating prediction models. Am J Epidemiol 173: 1327-1335, 2011
28. Pepe MS, Janes H: Commentary: Reporting standards are needed for evaluations of risk reclassification. Int J Epidemiol 40: 1106-1108, 2011
29. KerrKF,Wang Z, JanesH,McClelland RL,Psaty BM, Pepe MS: Net reclassification indices for evaluating risk prediction instruments:A critical review. Epidemiology 25: 114-121, 2014
30. Kerr KF, Bansal A, Pepe MS: Further insight into the incremental value of new markers: The interpretation of performance measures and the importance of clinical context. Am J Epidemiol 176: 482-487, 2012
31. Leening MJ, Vedder MM, Witteman JC, Pencina MJ, Steyerberg EW: Net reclassification improvement: Computation, interpretation, and controversies: A literature review and clinician's guide. Ann Intern Med 160: 122-131, 2014
32. Pickering JW, Endre ZH: New metrics for assessing diagnostic potential of candidate biomarkers. Clin J Am Soc Nephrol 7: 1355-1364, 2012
33. Sackett DL, Haynes RB: The architecture of diagnostic research. BMJ 324: 539-541, 2002
34. Steyerberg EW, Pencina MJ, Lingsma HF, Kattan MW, Vickers AJ, Van Calster B: Assessing the incremental value of diagnostic and prognostic markers: A review and illustration. Eur J Clin Invest 42: 216-228, 2012
35. Vickers AJ: Decision Curvey Analysis,NewYork, Memorial Sloan-Kettering Cancer Center, 2014
36. Van Calster B, Vickers AJ, Pencina MJ, Baker SG, Timmerman D, Steyerberg EW: Evaluation of markers and risk prediction models: Overview of relationships between NRI and decision-analytic measures. Med Decis Making 33: 490-501, 2013
37. Hlatky MA, Greenland P, Arnett DK, Ballantyne CM, CriquiMH, Elkind MS, Go AS, Harrell FE Jr.,Hong Y,Howard BV, Howard VJ, Hsue PY, Kramer CM, McConnell JP, Normand SL, O'Donnell CJ, Smith SC Jr., Wilson PW; 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: A scientific statement from the American Heart Association. Circulation 119: 2408-2416, 2009
38. Vickers AJ, Pepe MS: Does the net reclassification improvement help us evaluate models and markers? Ann Intern Med 160: 136-137, 2014