PREDICTIVE DIAGNOSTICS FOR LOGISTIC MODELS

✍ Scribed by FRANÇOISE SEILLIER-MOISEIWITSCH

• Publisher: John Wiley and Sons
• Year: 1996
• Tongue: English
• Weight: 784 KB
• Volume: 15
• Category: Article
• ISSN: 0277-6715
• DOI: 10.1002/(sici)1097-0258(19961030)15:20<2149::aid-sim360>3.0.co;2-h

✦ Synopsis

Novel methodology is implemented to assess the predictive power of covariate information associated with sequential binary events. Logistic models are first fitted on the basis of a subset of the observations and then evaluated sequentially on the rest. The probabilistic forecasts are compared to the outcomes via a scoring function, but as most validation samples are small, the usual reference distribution for the test statistics is inadequate. However, bootstrap-based distributions can easily be constructed. The first example pertains to the evaluation of screening tests for major depression. It illustrates that goodness-of-fit and predictive assessments lead to the selection of very different models. The second example deals with the prediction of a major event in the natural history of HIV-induced disease. It shows that this type of analysis can reveal features missed by other approaches.