Written by a well-known biostatistical researcher and expert on S-Plus
Provides full-scale case studies of non-trivial datasets
Case studies use S-PLUS
Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about 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 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 model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".|There are many books that are excellent sources of knowledge about individual stastical tools (survival models, general linear models, etc.), but the art of data analysis is about choosing and using multiple tools. In the words of Chatfield "...students typically know the technical details of regressin for example, but not necessarily when and how to apply it. This argues the need for a better balance in the literature and in statistical teaching between techniques and problem solving strategies." Whether analyzing risk factors, adjusting for biases in observational studies, or developing predictive models, there are common problems that few regression texts address. For example, there are missing data in the majority of datasets one is likely to encounter (other than those used in textbooks!) but most regression texts do not include methods for dealing with such data effectively, and texts on missing data do not cover regression modeling.|The book will serve as a reference for data analysts and statistical methodologists.
Introduction * General Aspects of Fitting Regression Models * Missing Data * Multivariable Modeling Strategies * Resampling, Validating, Describing, and Simplifying the Model * S-PLUS Software * Case Study in Least Squares Fitting and Interpretation of a Linear Model * Case Study in Imputation and Data Reduction * Overview of Maximum Likelihood Estimation * Binary Logistic Regression * Logistic Model Case Study 1: Predicting Cause of Death * Logistic Model Case Study 2: Survival of Titanic Passengers * Ordinal Logistic Regression * Case Study in Ordinal Regrssion, Data Reduction, and Penalization * Models Using Nonparametic Transformations of X and Y * 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
From the reviews:
TECHNOMETRICS
"The book is an ambitious, and mostly successful, attempt to disseminate effective strategies for the use of regression techniques. Many of the examples are from the medical area, in which the author has worked for many years and has accumulated a wealth of experience. It is written in a clear and direct style...definitely a valuable reference for modern applications of commonly used regression techniques. Data analysis, particularly users of S-PLUS, with experience in the application of these tools will benefit the most from this book."
SHORT BOOK REVIEWS
"This is a book that leaves one breathless. It demands a lot, but gives plenty in return. ... The book has many sets of programming instructions and printouts, all delivered in a stacato fashion. Sets of data are large. Many different types of models and methods are discussed. There are many printouts and diagrams. Computer oriented readers will like this book immediately. Others may grow to like it. It is an essential reference for the library."
STATISTICAL METHODS IN MEDICAL RESEARCH
"This is the latest volume in the generally excellent Springer Series in Statistics, and it has to be one of the best. Professor Harrell has produced a book that offers many new and imaginative insights into multiple regression, logistic regression and survival analysis, topics that form the core of much of the statistical analysis carried out in a variety of disciplines, particularly in medicine. ... Regression Modelling Stategies is a book that many statisticians will enjoy and learn from. The problems given at the end of each chapter may also make it suitable for some postgrdauate courses, particularly those for medical students in which S-PLUS is a major component. Working through the case studies in the book will demonstrate what can be achieved with a little imagination, when modelling complex and challenging data sets. So here we have a truly excellent, informative and attractive text that is highly recommended."
MEDICAL DECISION MAKING
"Over the past 7 years, I have probably read this book, on its preversion, a half-dozen times, and I refer to it routinely. If my work bookshelf held only one book, it would be this one. The book covers, very completely, the nuances of regression modeling with particular emphasis on binary and ordinal logistic regression and parametric and nonparametric survival analysis...Harrell very nicely walks the reader through numerous analyses, explaining and defining his model-building choices at each step in the process. It is refreshing to have an author present choices and actuallly defend an approach, and in this manner."
"This book emphasizes problem solving strategies that address the many issues arising when developing multivariable models ... . The author has a very motivating style and includes opinions, remarks and summary ... . The logical path chosen on how to present the material is excellent. ... considering the fun I had reading the book, I think that the author´s aims are met and I highly recommend everybody to have a look at the book. Moreover, I recommend purchasing the book to any library." (Diego Kuonen, Statistical Methods in Medical Research, Vol. 13 (5), 2004)
"It is a book that tries to show us how many different tools may be used in combination for regression analysis. ... The author gives us plenty of references (466!) to textbooks and papers where we may read more about individual topics; most chapters end with suggestions for further reading and problems. ... Many tools are illustrated in five chapter-long case studies. ... the author has written a very inspiring book which should be able to teach most of us something ... ." (Søren Feodor Nielsen, Journal of Applied Statistics, Vol. 30 (1), 2003)
"This book could serve as a wonderful textbook for a graduate-level or upper undergraduate-level data-analysis class. There are plenty of hands-on exercises ... . From a researcher´s perspective, there are enough interesting ideas to easily stimulate research on other fruitful avenues. From an applied statistician´s perspective, the book fills an important gap in the field and would serve as an ideal resource. ... a well laid-out, enjoyable book. I wholeheartedly recommend it ... to anyone interested in the strategies of intelligent data analysis." (Sunil J. Rao, Journal of the American Statistical Association, March, 2003)
"Regression Modeling Strategies is largely about prediction. ... The book is incredibly well referenced, with a 466-item bibliography. ... Harrell very nicely walks the reader through numerous analyses, explaining and defining his model-building choices at each step in the process. It is refreshing to have an author present choices and actually defend an approach ... . I found his arguments very convincing. Certainly, if you are interested in developing or validating prediction models, you will likely find this book to be very valuable." (Mike Kattan, Medical Decision Making, March/April, 2003)
"Professor Harrell provides deillegalscriptions of statistical strategies intended for the analysis of data using linear, logistic and proportional hazard regression models. ... Harrell combines statistical theory with a modest amount of mathematics, data in the form of case studies, implementation of regression models, graphics and interpretation making it attractive to Masters or PhD level graduate students as well as biomedical researchers. ... this is an excellent book for serious researchers." (Max K. Bulsara, Lab News, August/September, 2002)
Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about 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 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 model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining". There are many books that are excellent sources of knowledge about individual stastical tools (survival models, general linear models, etc.), but the art of data analysis is about choosing and using multiple tools. In the words of Chatfield "...students typically know the technical details of regressin for example, but not necessarily when and how to apply it. This argues the need for a better balance in the literature and in statistical teaching between techniques and problem solving strategies." Whether analyzing risk factors, adjusting for biases in observational studies, or developing predictive models, there are common problems that few regression texts address. For example, there are missing data in the majority of datasets one is likely to encounter (other than those used in textbooks!) but most regression texts do not include methods for dealing with such data effectively, and texts on missing data do not cover regression modeling.
1 Introduction.- 2 General Aspects of Fitting Regression Models.- 3 Missing Data.- 4 Multivariable Modeling Strategies.- 5 Resampling, Validating, Describing, and Simplifying the Model.- 6 S-Plus Software.- 7 Case Study in Least Squares Fitting and Interpretation of a Linear Model.- 8 Case Study in Imputation and Data Reduction.- 9 Overview of Maximum Likelihood Estimation.- 10 Binary Logistic Regression.- 11 Logistic Model Case Study 1: 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 Models Using Nonparametric Transformations of X and Y.- 16 Introduction to Survival Analysis.- 17 Parametric Survival Models.- 18 Case Study in Parametric Survival Modeling and Model Approximation.- 19 Cox Proportional Hazards Regression Model.- 20 Case Study in Cox Regression.
From the reviews:
TECHNOMETRICS
"The book is an ambitious, and mostly successful, attempt to disseminate effective strategies for the use of regression techniques. Many of the examples are from the medical area, in which the author has worked for many years and has accumulated a wealth of experience. It is written in a clear and direct style...definitely a valuable reference for modern applications of commonly used regression techniques. Data analysis, particularly users of S-PLUS, with experience in the application of these tools will benefit the most from this book."
SHORT BOOK REVIEWS
"This is a book that leaves one breathless. It demands a lot, but gives plenty in return. ... The book has many sets of programming instructions and printouts, all delivered in a stacato fashion. Sets of data are large. Many different types of models and methods are discussed. There are many printouts and diagrams. Computer oriented readers will like this book immediately. Others may grow to like it. It is an essential reference for the library."
STATISTICAL METHODS IN MEDICAL RESEARCH
"This is the latest volume in the generally excellent Springer Series in Statistics, and it has to be one of the best. Professor Harrell has produced a book that offers many new and imaginative insights into multiple regression, logistic regression and survival analysis, topics that form the core of much of the statistical analysis carried out in a variety of disciplines, particularly in medicine. ... Regression Modelling Stategies is a book that many statisticians will enjoy and learn from. The problems given at the end of each chapter may also make it suitable for some postgrdauate courses, particularly those for medical students in which S-PLUS is a major component. Working through the case studies in the book will demonstrate what can be achieved with a little imagination, when modelling complex and challenging data sets. So here we have a trulyexcellent, informative and attractive text that is highly recommended."
MEDICAL DECISION MAKING
"Over the past 7 years, I have probably read this book, on its preversion, a half-dozen times, and I refer to it routinely. If my work bookshelf held only one book, it would be this one. The book covers, very completely, the nuances of regression modeling with particular emphasis on binary and ordinal logistic regression and parametric and nonparametric survival analysis...Harrell very nicely walks the reader through numerous analyses, explaining and defining his model-building choices at each step in the process. It is refreshing to have an author present choices and actuallly defend an approach, and in this manner."
"This book emphasizes problem solving strategies that address the many issues arising when developing multivariable models ... . The author has a very motivating style and includes opinions, remarks and summary ... . The logical path chosen on how to present the material is excellent. ... considering the fun I had reading the book, I think that the author's aims are met and I highly recommend everybody to have a look at the book. Moreover, I recommend purchasing the book to any library." (Diego Kuonen, Statistical Methods in Medical Research, Vol. 13 (5), 2004)
"It is a book that tries to show us how many different tools may be used in combination for regression analysis. ... The author gives us plenty of references (466!) to textbooks and papers where we may read more about individual topics; most chapters end with suggestions for further reading and problems. ... Many tools are illustrated in five chapter-long case studies. ... the author has written a very inspiring book which should be able to teach most of us something ... ." (Søren Feodor Nielsen, Journal of Applied Statistics, Vol. 30 (1), 2003)
"This book could serve as a wonderful textbook for a graduate-level or upper undergraduate-level data-analysis class.There are plenty of hands-on exercises ... . From a researcher's perspective, there are enough interesting ideas to easily stimulate research on other fruitful avenues. From an applied statistician's perspective, the book fills an important gap in the field and would serve as an ideal resource. ... a well laid-out, enjoyable book. I wholeheartedly recommend it ... to anyone interested in the strategies of intelligent data analysis." (Sunil J. Rao, Journal of the American Statistical Association, March, 2003)
"Regression Modeling Strategies is largely about prediction. ... The book is incredibly well referenced, with a 466-item bibliography. ... Harrell very nicely walks the reader through numerous analyses, explaining and defining his model-building choices at each step in the process. It is refreshing to have an author present choices and actually defend an approach ... . I found his arguments very convincing. Certainly, if you are interested in developing or validating prediction models, you will likely find this book to be very valuable." (Mike Kattan, Medical Decision Making, March/April, 2003)
"Professor Harrell provides deillegalscriptions of statistical strategies intended for the analysis of data using linear, logistic and proportional hazard regression models. ... Harrell combines statistical theory with a modest amount of mathematics, data in the form of case studies, implementation of regression models, graphics and interpretation making it attractive to Masters or PhD level graduate students as well as biomedical researchers. ... this is an excellent book for serious researchers." (Max K. Bulsara, Lab News, August/September, 2002)
Inhaltsverzeichnis
Introduction * General Aspects of Fitting Regression Models * Missing Data * Multivariable Modeling Strategies * Resampling, Validating, Describing, and Simplifying the Model * S-PLUS Software * Case Study in Least Squares Fitting and Interpretation of a Linear Model * Case Study in Imputation and Data Reduction * Overview of Maximum Likelihood Estimation * Binary Logistic Regression * Logistic Model Case Study 1: Predicting Cause of Death * Logistic Model Case Study 2: Survival of Titanic Passengers * Ordinal Logistic Regression * Case Study in Ordinal Regrssion, Data Reduction, and Penalization * Models Using Nonparametic Transformations of X and Y * 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
Klappentext
Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about 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 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 model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".
The book will serve as a reference for data analysts and statistical methodologists.
