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Logistic regression models are widely used in medicine for predicting patient outcome (prognosis) and constructing diagnostic tests (diagnosis). Multivariable logistic models yield an (approximately) continuous risk score, a transformation of which gives the estimated event probability for an individual. A key aspect of model performance is discrimination, that is, the model's ability to distinguish between patients who have (or will have) an event of interest and those who do not (or will not). Graphical aids are important in understanding a logistic model. The receiver-operating characteristic (ROC) curve is familiar, but not necessarily easy to interpret. We advocate a simple graphic that provides further insight into discrimination, namely a histogram or dot plot of the risk score in the outcome groups. The most popular performance measure for the logistic model is the c-index, numerically equivalent to the area under the ROC curve. We discuss the comparative merits of the c-index and the (standardized) mean difference in risk score between the outcome groups. The latter statistic, sometimes known generically as the effect size, has been computed in slightly different ways by several different authors, including Glass, Cohen and Hedges. An alternative measure is the overlap between the distributions in the outcome groups, defined as the area under the minimum of the two density functions. The larger the overlap, the weaker the discrimination. Under certain assumptions about the distribution of the risk score, the c-index, effect size and overlap are functionally related. We illustrate the ideas with simulated and real data sets.

Original publication

DOI

10.1002/sim.3994

Type

Journal article

Journal

Statistics in medicine

Publication Date

10/2010

Volume

29

Pages

2508 - 2520

Addresses

MRC Clinical Trials Unit, 222 Euston Road, London NW12DA, UK. pr@ctu.mrc.ac.uk

Keywords

Humans, Breast Neoplasms, Coronary Disease, Prognosis, Treatment Outcome, Multivariate Analysis, Confidence Intervals, Discriminant Analysis, Logistic Models, Risk Assessment, Predictive Value of Tests, ROC Curve, Data Display, Decision Support Techniques, Female