Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis

Table of content

1 Introduction
2 General Aspects of Fitting Regression Models
3 Missing Data
4 Multivariable Modeling Strategies
5 Describing, Resampling, Validating, and Simplifying the Model
6 R Software
7 Modeling Longitudinal Responses using Generalized Least Squares
8 Case Study in Data Reduction
9 Overview of Maximum Likelihood Estimation
10 Binary Logistic Regression
11 Case Study in Binary Logistic Regression, Model Selection and Approximation: Predicting Cause of Death
12 Logistic Model Case Study 2: Survival of Titanic Passengers
13 Ordinal Logistic Regression
14 Case Study in Ordinal Regression, Data Reduction, and Penalization
15 Regression Models for Continuous Y and Case Study in Ordinal Regression
16 Transform-Both-Sides Regression
17 Introduction to Survival Analysis
18 Parametric Survival Models
19 Case Study in Parametric Survival Modeling and Model Approximation
20 Cox Proportional Hazards Regression Model
21 Case Study in Cox Regression

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.

https://www.springer.com/gp/book/9783319194240