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...

Full description

Saved in:
Bibliographic Details
Published in:American journal of epidemiology 2011-08, Vol.174 (3), p.364-374
Main Authors: Kerr, Kathleen F., McClelland, Robyn L., Brown, Elizabeth R., Lumley, Thomas
Format: Article
Language:English
Subjects:
Bias
Biological and medical sciences
Biomarkers
Bootstrap method
Data Interpretation, Statistical
Epidemiology
Estimation bias
General aspects
Health risk assessment
HIV Infections - transmission
Humans
Infectious Disease Transmission, Vertical - statistics & numerical data
Logistic Models
Medical sciences
Miscellaneous
Models, Statistical
Monte Carlo Method
Practice of Epidemiology
Predictive Value of Tests
Public health. Hygiene
Public health. Hygiene-occupational medicine
Reproducibility of Results
Risk Assessment
ROC Curve
Sampling techniques
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0002-9262
1476-6256
DOI:10.1093/aje/kwr086