Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis (Springer Series in Statistics)

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 modeling, which entails choosing and using multiple tools. 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 fitting 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 model validation techniques based on the bootstrap. The reader will gain a keen understanding of predictive accuracy and the harm of categorizing continuous predictors or outcomes. 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 non-statisticians. Regression Modeling Strategies presents full-scale case studies of non-trivial datasets 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 generalized 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 Ph.D. 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 modeling techniques. Examples used in the text mostly come from biomedical research, but the methods are applicable anywhere predictive models ("analytics") are useful, including economics, epidemiology, sociology, psychology, engineering and marketing. 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 Modeling, Which Entails Choosing And Using Multiple Tools. 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 Fitting 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 Model Validation Techniques Based On The Bootstrap. The Reader Will Gain A Keen Understanding Of Predictive Accuracy, And The Harm Of Categorizing Continuous Predictors Or Outcomes. 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 Non-statisticians. Regression Modeling Strategies Presents Full-scale Case Studies Of Non-trivial Datasets 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 Generalized Least Squares For Longitudinal Data, The Binary Logistic Model, Models For Ordinal Responses, Parametric Survival Regression Models, And The Cox Semiparametric 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 Ph.d. 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 Modeling Techniques. Examples Used In The Text Mostly Come From Biomedical Research, But The Methods Are Applicable Anywhere Predictive Models (analytics) Are Useful, Including Economics, Epidemiology, Sociology, Psychology, Engineering, And Marketing. 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. . By Frank E. Harrell , Jr. 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 modeling, which entails choosing and using multiple tools. 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 fitting 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 model validation techniques based on the bootstrap.℗l The reader will gain a keen understanding of predictive accuracy, and the harm of categorizing continuous predictors or outcomes.℗l 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 non-statisticians. Regression Modeling Strategies presents full-scale case studies of non-trivial datasets 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 generalized least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models, and the Cox semiparametric survival model.℗l 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 Ph. D. 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 modeling techniques. Examples used in the text mostly come from biomedical research, but the methods are applicable anywhere predictive models ("analytics") are useful, including economics, epidemiology, sociology, psychology, engineering, and marketing

Now in widespread use, generalized additive models (GAMs) have evolved into a standard statistical methodology of considerable flexibility. While Hastie and Tibshirani's outstanding 1990 research monograph on GAMs is largely responsible for this, there has been a long-standing need for an accessible introductory treatment of the subject that also emphasizes recent penalized regression spline approaches to GAMs and the mixed model extensions of these models.

Generalized Additive Models: An Introduction with R imparts a thorough understanding of the theory and practical applications of GAMs and related advanced models, enabling informed use of these very flexible tools. The author bases his approach on a framework of penalized regression splines, and builds a well-grounded foundation through motivating chapters on linear and generalized linear models. While firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of the freely available R software helps explain the theory and illustrates the practicalities of linear, generalized linear, and generalized additive models, as well as their mixed effect extensions.

The treatment is rich with practical examples, and it includes an entire chapter on the analysis of real data sets using R and the author's add-on package mgcv. Each chapter includes exercises, for which complete solutions are provided in an appendix.

Concise, comprehensive, and essentially self-contained, Generalized Additive Models: An Introduction with R prepares readers with the practical skills and the theoretical background needed to use and understand GAMs and to move on to other GAM-related methods and models, such as SS-ANOVA, P-splines, backfitting and Bayesian approaches to smoothing and additive modelling.

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. 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 ModellingStrategies presents full-scale case studies of non-trivial data-setsinstead 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 generalisedleast 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. "Generalized Additive Models: An Introduction with R imparts a thorough understanding of the theory and practical applications of GAMs and related advanced models, enabling informed use of these very flexible tools. The author bases his approach on a framework of penalized regression splines, and builds a well-grounded foundation through motivating chapters on linear and generalized linear models. While firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of the freely available R software helps explain the theory and illustrates the practicalities of linear, generalized linear, and generalized additive models, as well as their mixed effect extensions. The treatment is rich with practical examples, and it includes an entire chapter on the analysis of real data sets using R and the author's add-on package mgcv."--BOOK JACKET.

This book describes an array of power tools for data analysis that are based on nonparametric regression and smoothing techniques. These methods relax the linear assumption of many standard models and allow analysts to uncover structure in the data that might otherwise have been missed. While McCullagh and Nelder's Generalized Linear Models shows how to extend the usual linear methodology to cover analysis of a range of data types, Generalized Additive Models enhances this methodology even further by incorporating the flexibility of nonparametric regression. Clear prose, exercises in each chapter, and case studies enhance this popular text.

Imparts a thorough understanding of the theory and practical applications of GAMs and related advanced models, enabling informed use of these flexible tools. The author's approach is based on a framework of penalized regression splines, and he builds the necessary foundation through motivating chapters on linear and generalized linear models. A variety of power tools for data analysis, based on non-parametric regression or smoothing techniques, are described in this text. These methods relax the usual linear assumption in many standard models, allowing the analyst to uncover structure in data