Assessing the clinical impact of risk prediction models with decision curves: Guidance for correct interpretation and appropriate use

Journal of Clinical Oncology

Volumn 34, Issue 21, 2016, Pages 2534-2540

  Kerr, Kathleen F ^a   Brown, Marshall D ^b   Zhu, Kehao ^a   Janes, Holly ^b  

^a UNIVERSITY OF WASHINGTON   (United States)

^b USA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CANCER RISK; CLINICAL DECISION MAKING; CLINICAL EVALUATION; CONCEPTUAL FRAMEWORK; DECISION CURVE; HEALTH IMPACT ASSESSMENT; HUMAN; PLOTS AND CURVES; PREDICTION; PRIORITY JOURNAL; PROSTATE BIOPSY; PROSTATE CANCER; RECEIVER OPERATING CHARACTERISTIC; RISK ASSESSMENT; TUMOR BIOPSY; DECISION MAKING; DECISION SUPPORT SYSTEM; MALE; PROSTATECTOMY; PROSTATIC NEOPLASMS; RISK; SOFTWARE; THEORETICAL MODEL;

DECISION MAKING; DECISION SUPPORT TECHNIQUES; HUMANS; MALE; MODELS, THEORETICAL; PROSTATECTOMY; PROSTATIC NEOPLASMS; RISK; SOFTWARE;

EID: 84978180051     PISSN: 0732183X     EISSN: 15277755     Source Type: Journal    
DOI: 10.1200/JCO.2015.65.5654     Document Type: Article

References (22)

1. Vickers AJ, Elkin EB: Decision curve analysis: A novel method for evaluating prediction models. Med Decis Making 26:565-574, 2006
2. Secin FP, Bianco FJ, Cronin A, et al: Is it necessary to remove the seminal vesicles completely at radical prostatectomy? Decision curve analysis of European Society of Urologic Oncology criteria. J Urol 181:609-613, 2009; discussion 614
3. Ang SF, Ng ES, Li H, et al: Correction: The Singapore Liver Cancer Recurrence (SLICER) score for relapse prediction in patients with surgically resected hepatocellular carcinoma. PLoS One 10: e0128058, 2015
4. Li J, Liu Y, Yan Z, et al: A nomogram predicting pulmonary metastasis of hepatocellular carcinoma following partial hepatectomy. Br J Cancer 110: 1110-1117, 2014
5. Scattoni V, Lazzeri M, Lughezzani G, et al: Head-to-head comparison of prostate health index and urinary PCA3 for predicting cancer at initial or repeat biopsy. J Urol 190:496-501, 2013
6. Den RB, Yousefi K, Trabulsi EJ, et al: Genomic classifier identifies men with adverse pathology after radical prostatectomy who benefit from adjuvant radiation therapy. J Clin Oncol 33:944-951, 2015
7. Labidi M, Lavoie P, Lapointe G, et al: Predicting success of endoscopic third ventriculostomy: Validation of the ETV Success Score in a mixedpopulation of adult and pediatric patients. J Neurosurg 123:1447-1455, 2015
8. McMahon PJ, Panczykowski DM, Yue JK, et al: Measurement of the glial fibrillary acidic protein and its breakdown products GFAP-BDP biomarker for the detection of traumatic brain injury compared to computed tomography and magnetic resonance imaging. J Neurotrauma 32:527-533, 2015
9. Matignon M, Ding R, Dadhania DM, et al:Urinary cell mRNA profiles and differential diagnosis of acute kidney graft dysfunction. J Am Soc Nephrol 25:1586-1597, 2014
10. Slankamenac K, Beck-Schimmer B, Breitenstein S, et al: Novel prediction score including preand intraoperative parameters best predicts acute kidney injury after liver surgery. World J Surg 37: 2618-2628, 2013
11. Baart AM, de Kort WL, Moons KG, et al: Zinc protoporphyrin levels have added value in the prediction of low hemoglobin deferral in whole blood donors. Transfusion 53:1661-1669, 2013
12. Raji OY, Duffy SW, Agbaje OF, et al: Predictive accuracy of the Liverpool Lung Project risk model for stratifying patients for computed tomography screening for lung cancer: A case-control and cohort validation study. Ann Intern Med 157:242-250, 2012
13. Peirce CS: The numerical measure of the success of predictions. Science 4:453-454, 1884
14. Baker SG: Putting risk prediction in perspective: Relative utility curves. J Natl Cancer Inst 101: 1538-1542, 2009
15. Baker SG, Cook NR, Vickers A, et al: Using relative utility curves to evaluate risk prediction. J R Stat Soc Ser A Stat Soc 172:729-748, 2009
16. Baker SG, Kramer BS: Evaluating prognostic markers using relative utility curves and test tradeoffs. J Clin Oncol 33:2578-2580, 2015
17. Lughezzani G, Zorn KC, Budäus L, et al: Comparison of three different tools for prediction of seminal vesicle invasion at radical prostatectomy. Eur Urol 62:590-596, 2012
18. Pepe MS, Fan J, Feng Z, et al: The Net Reclassification Index (NRI): A misleading measure of prediction improvement even with independent test data sets. Stat Biosci 7:282-295, 2015
19. Van Calster B, Vickers AJ: Calibration of risk prediction models: Impact on decision-analytic performance. Med Decis Making 35:162-169, 2015
20. Vickers AJ, Cronin AM: Traditional statistical methods for evaluating prediction models are uninformative as to clinical value: Towards a decision analytic framework. Semin Oncol 37:31-38, 2010
21. Bryant RJ, Sjoberg DD, Vickers AJ, et al: Predicting high-grade cancer at ten-core prostate biopsy using four kallikrein markers measured in blood in the ProtecT study. J Natl Cancer Inst 107:7, 2015
22. Bossuyt PM, Reitsma JB, Linnet K, et al: Beyond diagnostic accuracy: The clinical utility of diagnostic tests. Clin Chem 58:1636-1643, 2012