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Evaluating the Incremental Value of New Biomarkers With Integrated Discrimination Improvement.

The integrated discrimination improvement (IDI) index is a popular tool for evaluating the capacity of a marker to predict a binary outcome of interest. Recent reports have proposed that the IDI is more sensitive than other metrics for identifying useful predictive markers. In this article, the auth...

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Published in:American Journal of Epidemiology Vol. 174; no. 3; pp. 364 - 375
Main Authors: Kerr, Kathleen F., McClelland, Robyn L., Brown, Elizabeth R., Lumley, Thomas
Format: equations & formulas research tables/charts Journal Article
Published: Oxford University Press / USA Aug2011
Online Access:View this record in EBSCOhost
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      jtl: American Journal of Epidemiology
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      dt: Aug2011
      vid: 174
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      pub: Oxford University Press / USA
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        10.1093/aje/kwr086
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        atl: Evaluating the Incremental Value of New Biomarkers With Integrated Discrimination Improvement.
      aug:
        au:
          Kerr, Kathleen F.
          McClelland, Robyn L.
          Brown, Elizabeth R.
          Lumley, Thomas
      sug:
        subj:
          Biological Markers
          Discriminant Validity
          Predictive Validity
          Sensitivity and Specificity Evaluation
          Human
          Funding Source
          Epidemiological Research Methods
          Computer Simulation Utilization
          Null Hypothesis
          Validation Studies
          Data Analysis, Statistical Methods
          Type I Error
          Odds Ratio
          Logistic Regression
          Human Immunodeficiency Virus Transmission
          Sampling Error
      ab: The integrated discrimination improvement (IDI) index is a popular tool for evaluating the capacity of a marker to predict a binary outcome of interest. Recent reports have proposed that the IDI is more sensitive than other metrics for identifying useful predictive markers. In this article, the authors use simulated data sets and theoretical analysis to investigate the statistical properties of the IDI. The authors consider the common situation in which a risk model is fitted to a data set with and without the new, candidate predictor(s). Results demonstrate that the published method of estimating the standard error of an IDI estimate tends to underestimate the error. The z test proposed in the literature for IDI-based testing of a new biomarker is not valid, because the null distribution of the test statistic is not standard normal, even in large samples. If a test for the incremental value of a marker is desired, the authors recommend the test based on the model. For investigators who find the IDI to be a useful measure, bootstrap methods may offer a reasonable option for inference when evaluating new predictors, as long as the added predictive capacity is large.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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