Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cb9f654ff-lqqdg Total loading time: 0 Render date: 2025-08-09T00:49:20.857Z Has data issue: false hasContentIssue false

Chapter 6 - Risk Predictions

Published online by Cambridge University Press:  02 May 2020

Thomas B. Newman  and
Michael A. Kohn
Thomas B. Newman
Affiliation:
University of California, San Francisco
Michael A. Kohn
Affiliation:
University of California, San Francisco
Get access

Summary

In previous chapters, we discussed issues affecting evaluation and use of diagnostic tests: how to assess test reliability and accuracy, how to combine the results of tests with prior information to estimate disease probability, and how a test’s value depends on the decision it will guide and the relative cost of errors. In this chapter, we move from diagnosing prevalent disease to predicting incident outcomes. We will discuss the difference between diagnostic tests and risk predictions and then focus on evaluating predictions, specifically covering calibration, discrimination, net benefit calculations, and decision curves.

Information

Type
Chapter
Information
Evidence-Based Diagnosis
An Introduction to Clinical Epidemiology
 , pp. 144 - 174
Publisher: Cambridge University Press
Print publication year: 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

References

Webster’s. Random House Webster’s unabridged dictionary. 2nd ed. New York: Random House Reference; 2001. 2230pp.Google Scholar
May, MT, Vehreschild, JJ, Trickey, A, et al. Mortality according to CD4 count at start of combination antiretroviral therapy among HIV-infected patients followed for up to 15 years after start of treatment: collaborative cohort study. Clin Infect Dis. 2016;62(12):1571–7.Google Scholar
Jellema, P, van der Windt, DA, van der Horst, HE, Stalman, WA, Bouter, LM. Prediction of an unfavourable course of low back pain in general practice: comparison of four instruments. Br J Gen Pract. 2007;57(534):15–22.Google ScholarPubMed
Lemeshow, S, Teres, D, Klar, J, et al. Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients. JAMA. 1993;270(20):2478–86.Google Scholar
Higgins, TL, Teres, D, Copes, WS, et al. Assessing contemporary intensive care unit outcome: an updated Mortality Probability Admission Model (MPM0-III). Crit Care Med. 2007;35(3):827–35. https://journals.lww.com/ccmjournal/Abstract/2007/03000/Assessing_contemporary_intensive_care_unit.21.aspx.Google Scholar
Vickers, AJ, Van Calster, B, Steyerberg, EW. Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests. BMJ. 2016;352:i6.Google Scholar
Collins, GS, Altman, DG. Predicting the 10 year risk of cardiovascular disease in the United Kingdom: independent and external validation of an updated version of QRISK2. BMJ. 2012;344:e4181.CrossRefGoogle ScholarPubMed
Hilsenbeck, SG, Clark, GM, McGuire, WL. Why do so many prognostic factors fail to pan out? Breast Cancer Res Treat. 1992;22(3):197–206.CrossRefGoogle ScholarPubMed
Kyzas, PA, Loizou, KT, Ioannidis, JP. Selective reporting biases in cancer prognostic factor studies. J Natl Cancer Inst. 2005;97(14):1043–55.Google Scholar
Hilden, J, Gerds, TA. A note on the evaluation of novel biomarkers: do not rely on integrated discrimination improvement and net reclassification index. Stat Med. 2014;33(19):3405–14.CrossRefGoogle Scholar
Kerr, KF, Janes, H. First things first: risk model performance metrics should reflect the clinical application. Stat Med. 2017;36(28):4503–8.CrossRefGoogle ScholarPubMed
Pepe, MS, Fan, J, Feng, Z, Gerds, T, Hilden, J. The net reclassification index (NRI): a misleading measure of prediction improvement even with independent test data sets. Stat Biosci. 2015;7(2):282–95.Google Scholar
Paik, S, Shak, S, Tang, G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351(27):2817–26.Google Scholar
Goodson, WH 3rd. Molecular prediction of recurrence of breast cancer. N Engl J Med. 2005;352(15):1605–7.Google Scholar
Tanvetyanon, T. Molecular prediction of recurrence of breast cancer. N Engl J Med. 2005;352(15):1605–7.Google Scholar
Hafler, DA, Compston, A, Sawcer, S, et al. Risk alleles for multiple sclerosis identified by a genomewide study. N Engl J Med. 2007;357(9):851–62.Google ScholarPubMed
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. 2008;27(2):157–72; discussion 207–12.CrossRefGoogle ScholarPubMed

References

Johnston, SC, Rothwell, PM, Nguyen-Huynh, MN, et al. Validation and refinement of scores to predict very early stroke risk after transient ischaemic attack. Lancet. 2007;369(9558):283–92.Google Scholar
Schuetz, P, Koller, M, Christ-Crain, M, et al. Predicting mortality with pneumonia severity scores: importance of model recalibration to local settings. Epidemiol Infect. 2008;136(12):1628–37.Google Scholar
Ridker, PM, Cook, NR. Statins: new American guidelines for prevention of cardiovascular disease. Lancet. 2013;382(9907):1762–5.CrossRefGoogle ScholarPubMed
Cook, NR, Ridker, PM. Calibration of the pooled cohort equations for atherosclerotic cardiovascular disease: an update. Ann Intern Med. 2016;165(11):786–94.Google Scholar
Ridker, PM, Cook, NR. The pooled cohort equations 3 years on: building a stronger foundation. Circulation. 2016;134(23):1789–91.Google Scholar

Accessibility standard: Unknown

Accessibility compliance for the PDF of this book is currently unknown and may be updated in the future.

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • Risk Predictions
  • Thomas B. Newman, University of California, San Francisco, Michael A. Kohn, University of California, San Francisco
  • Book: Evidence-Based Diagnosis
  • Online publication: 02 May 2020
  • Chapter DOI: https://doi.org/10.1017/9781108500111.007
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • Risk Predictions
  • Thomas B. Newman, University of California, San Francisco, Michael A. Kohn, University of California, San Francisco
  • Book: Evidence-Based Diagnosis
  • Online publication: 02 May 2020
  • Chapter DOI: https://doi.org/10.1017/9781108500111.007
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Risk Predictions
  • Thomas B. Newman, University of California, San Francisco, Michael A. Kohn, University of California, San Francisco
  • Book: Evidence-Based Diagnosis
  • Online publication: 02 May 2020
  • Chapter DOI: https://doi.org/10.1017/9781108500111.007
Available formats
×