title: Evaluating the incremental value of new biomarkers with integrated discrimination improvement.
abstract: 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.
authors.affiliation: Department of Biostatistics, School of Public Health, University of Washington, Seattle, USA. katiek@u.washington.edu
authors.forename: Kathleen F
authors.initials: KF
authors.lastname: Kerr
authors.forename: Robyn L
authors.initials: RL
authors.lastname: McClelland
authors.forename: Elizabeth R
authors.initials: ER
authors.lastname: Brown
authors.forename: Thomas
authors.initials: T
authors.lastname: Lumley
mesh_terms: D015982:Bias; D015415:Biomarkers; D003627:Data Interpretation, Statistical; D015658:HIV Infections / Q000635:transmission; D006801:Humans; D018445:Infectious Disease Transmission, Vertical / Q000706:statistics & numerical data; D016015:Logistic Models; D015233:Models, Statistical; D009010:Monte Carlo Method; D011237:Predictive Value of Tests; D012372:ROC Curve; D015203:Reproducibility of Results; D018570:Risk Assessment
chemical_list: D015415:Biomarkers