Regression Modeling Strategies

This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modelling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasises problem solving strategies that address the many issues arising when developing multi-variable models using real data and not standard textbook examples.

Regression Modelling Strategies presents full-scale case studies of non-trivial data-sets instead of over-simplified illustrations of each method. These case studies use freely available 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 multiple regression using generalised least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models and the Cox semi parametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression. As in the first edition, this text is intended for Masters' or PhD. level graduate students who have had a general introductory probability and statistics course and who are well versed in ordinary multiple regression and intermediate algebra. The book will also serve as a reference for data analysts and statistical methodologists, as it contains an up-to-date survey and bibliography of modern statistical modelling techniques.

Fully revised new edition features new material and color figures Published with mature, supplementary R package: rms New chapters and sections on generalized least squares for analysis of serial response data, redundancy analysis, bootstrap confidence intervals for rankings of predictors, expanded material on multiple imputation and predictive mean matching and more Includes supplementary material: sn.pub/extras

Auteur
Frank E. Harrell, Jr. is Professor of Biostatistics and Chair, Department of Biostatistics, Vanderbilt University School of Medicine, Nashville. He has developed numerous methods for predictive modeling, quantifying predictive accuracy and model validation and has published numerous predictive models and articles on applied statistics, medical research and clinical trials. He is on the editorial board for several biomedical and methodologic journals. He is a Fellow of the American Statistical Association (ASA) and a consultant to the U.S. Food and Drug Administration and to the pharmaceutical industry. He teaches a graduate course in regression modeling strategies and a course in biostatistics for medical researchers. In 2014 he was chosen to receive the WJ Dixon Award for Excellence in Statistical Consulting by the ASA.

Contenu

Introduction.- General Aspects of Fitting Regression Models.- Missing Data.- Multivariable Modeling Strategies.- Describing, Resampling, Validating and Simplifying the Model.- R Software.- Modeling Longitudinal Responses using Generalized Least Squares.- Case Study in Data Reduction.- Overview of Maximum Likelihood Estimation.- Binary Logistic Regression.- Binary Logistic Regression Case Study 1.- Logistic Model Case Study 2: Survival of Titanic Passengers.- Ordinal Logistic Regression.- Case Study in Ordinal Regression, Data Reduction and Penalization.- Regression Models for Continuous Y and Case Study in Ordinal Regression.- Transform-Both-Sides Regression.- Introduction to Survival Analysis.- Parametric Survival Models.- Case Study in Parametric Survival Modeling and Model Approximation.- Cox Proportional Hazards Regression Model.- Case Study in Cox Regression.- Appendix.