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Résumé :
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Instead of presenting isolated techniques, this text emphasizes problem-solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations", and powerful validation techniques based on the bootstrap. This text realistically deals with model uncertainty, and its effects on inference, to achieve "safe data mining". It also presents many graphical methods for communicating complex regression models to nonstatisticians. This document presents full-scale case studies of nontrivial datasets instead of over- simplified illustrations of each method. These case studies use freely available, high-level, S-PLUS and R functions that make the multiple imputation, model building, validation, and interpretation tasks described in the book relatively easy to do. Most of the methods in this text apply to all regression models, but special emphasis is given to some of the most popular ones : the linear multiple regression model based on ordinary least squares, the binary logistic model, two logistic models far ordinal responses, parametric survival regression models, and the Cox semiparametric survival model.
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