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PREDICTIVE DIAGNOSTICS FOR LOGISTIC MODELS
Author(s) -
SEILLIERMOISEIWITSCH FRANÇOISE
Publication year - 1996
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19961030)15:20<2149::aid-sim360>3.0.co;2-h
Subject(s) - covariate , predictive power , logistic regression , statistics , computer science , probabilistic logic , goodness of fit , model selection , econometrics , machine learning , artificial intelligence , mathematics , philosophy , epistemology
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.