Integrates conceptual and computational treatment of tree representations of complex pathways to important outcomes across diverse scientific applicationsIntroduces random and alternative deterministic forests to facilitate interpretability of pathways with many contributing conditions and non-linear relationshipsIllustrates the interplay between scientific judgments and constraints on allowed pathway constructions; comparisons with conventional statistical methods
Integrates conceptual and computational treatment of tree representations of complex pathways to important outcomes across diverse scientific applications
Introduces random and alternative deterministic forests to facilitate interpretability of pathways with many contributing conditions and non-linear relationships
Illustrates the interplay between scientific judgments and constraints on allowed pathway constructions; comparisons with conventional statistical methods
Includes supplementary material: sn.pub/extras
Multiple complex pathways, characterized by interrelated events and c- ditions, represent routes to many illnesses, diseases, and ultimately death. Although there are substantial data and plausibility arguments suppo- ing many conditions as contributory components of pathways to illness and disease end points, we have, historically, lacked an e?ective method- ogy for identifying the structure of the full pathways. Regression methods, with strong linearity assumptions and data-basedconstraints onthe extent and order of interaction terms, have traditionally been the strategies of choice for relating outcomes to potentially complex explanatory pathways. However, nonlinear relationships among candidate explanatory variables are a generic feature that must be dealt with in any characterization of how health outcomes come about. It is noteworthy that similar challenges arise from data analyses in Economics, Finance, Engineering, etc. Thus, the purpose of this book is to demonstrate the e?ectiveness of a relatively recently developed methodology—recursive partitioning—as a response to this challenge. We also compare and contrast what is learned via rec- sive partitioning with results obtained on the same data sets using more traditional methods. This serves to highlight exactly where—and for what kinds of questions—recursive partitioning–based strategies have a decisive advantage over classical regression techniques.
A Practical Guide to Tree Construction.- Logistic Regression.- Classification Trees for a Binary Response.- Examples Using Tree-Based Analysis.- Random and Deterministic Forests.- Analysis of Censored Data: Examples.- Analysis of Censored Data: Concepts and Classical Methods.- Analysis of Censored Data: Survival Trees and Random Forests.- Regression Trees and Adaptive Splines for a Continuous Response.- Analysis of Longitudinal Data.- Analysis of Multiple Discrete Responses.
The routes to many important outcomes including diseases and ultimately death as well as financial credit consist of multiple complex pathways containing interrelated events and conditions. We have historically lacked effective methodologies for identifying these pathways and their non-linear and interacting features. This book focuses on recursive partitioning strategies as a response to the challenge of pathway characterization. A highlight of the second edition is the many worked examples, most of them from epidemiology, bioinformatics, molecular genetics, physiology, social demography, banking, and marketing. The statistical issues, conceptual and computational, are not only treated in detail in the context of important scientific questions, but also an array of substantively-driven judgments are explicitly integrated in the presentation of examples.Going considerably beyond the standard treatments of recursive partitioning that focus on pathway representations via single trees, this second edition has entirely new material devoted to forests from predictive and interpretive perspectives. For contexts where identification of factors contributing to outcomes is a central issue, both random and deterministic forest generation methods are introduced via examples in genetics and epidemiology. The trees in deterministic forests are reproducible and more easily interpretable than the components of random forests. Also new in the second edition is an extensive treatment of survival forests and post-market evaluation of treatment effectiveness.Heping Zhang is Professor of Public Health, Statistics, and Child Study, and director of the Collaborative Center for Statistics in Science, at Yale University. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, a Myrto Lefkopoulou Distinguished Lecturer Awarded by Harvard School of Public Health, and a Medallion lecturer selected by the Institute of Mathematical Statistics.Burton Singer is Courtesy Professor in the Emerging Pathogens Institute at University of Florida, and previously Charles and Marie Robertson Professor of Public and International Affairs at Princeton University. He is a member of the National Academy of Sciences and Institute of Medicine of the National Academies, and a Fellow of the American Statistical Association.
Multiple complex pathways, characterized by interrelated events and c- ditions, represent routes to many illnesses, diseases, and ultimately death. Although there are substantial data and plausibility arguments suppo- ing many conditions as contributory components of pathways to illness and disease end points, we have, historically, lacked an e?ective method- ogy for identifying the structure of the full pathways. Regression methods, with strong linearity assumptions and data-basedconstraints onthe extent and order of interaction terms, have traditionally been the strategies of choice for relating outcomes to potentially complex explanatory pathways. However, nonlinear relationships among candidate explanatory variables are a generic feature that must be dealt with in any characterization of how health outcomes come about. It is noteworthy that similar challenges arise from data analyses in Economics, Finance, Engineering, etc. Thus, the purpose of this book is to demonstrate the e?ectiveness of a relatively recently developed methodology-recursive partitioning-as a response to this challenge. We also compare and contrast what is learned via rec- sive partitioning with results obtained on the same data sets using more traditional methods. This serves to highlight exactly where-and for what kinds of questions-recursive partitioning-based strategies have a decisive advantage over classical regression techniques.
Über den Autor
Heping Zhang is Professor of Public Health, Statistics, and Child Study, and director of the Collaborative Center for Statistics in Science, at Yale University. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, a Myrto Lefkopoulou Distinguished Lecturer Awarded by Harvard School of Public Health, and a Medallion lecturer selected by the Institute of Mathematical Statistics.nBurton Singer is Courtesy Professor in the Emerging Pathogens Institute at University of Florida, and previously Charles and Marie Robertson Professor of Public and International Affairs at Princeton University. He is a member of the National Academy of Sciences and Institute of Medicine of the National Academies, and a Fellow of the American Statistical Association.
Inhaltsverzeichnis
A Practical Guide to Tree Construction.- Logistic Regression.- Classification Trees for a Binary Response.- Examples Using Tree-Based Analysis.- Random and Deterministic Forests.- Analysis of Censored Data: Examples.- Analysis of Censored Data: Concepts and Classical Methods.- Analysis of Censored Data: Survival Trees and Random Forests.- Regression Trees and Adaptive Splines for a Continuous Response.- Analysis of Longitudinal Data.- Analysis of Multiple Discrete Responses.
Klappentext
Multiple complex pathways, characterized by interrelated events and c- ditions, represent routes to many illnesses, diseases, and ultimately death. Although there are substantial data and plausibility arguments suppo- ing many conditions as contributory components of pathways to illness and disease end points, we have, historically, lacked an e?ective method- ogy for identifying the structure of the full pathways. Regression methods, with strong linearity assumptions and data-basedconstraints onthe extent and order of interaction terms, have traditionally been the strategies of choice for relating outcomes to potentially complex explanatory pathways. However, nonlinear relationships among candidate explanatory variables are a generic feature that must be dealt with in any characterization of how health outcomes come about. It is noteworthy that similar challenges arise from data analyses in Economics, Finance, Engineering, etc. Thus, the purpose of this book is to demonstrate the e?ectiveness of a relatively recently developed methodology-recursive partitioning-as a response to this challenge. We also compare and contrast what is learned via rec- sive partitioning with results obtained on the same data sets using more traditional methods. This serves to highlight exactly where-and for what kinds of questions-recursive partitioning-based strategies have a decisive advantage over classical regression techniques.
Integrates conceptual and computational treatment of tree representations of complex pathways to important outcomes across diverse scientific applications
Introduces random and alternative deterministic forests to facilitate interpretability of pathways with many contributing conditions and non-linear relationships
Illustrates the interplay between scientific judgments and constraints on allowed pathway constructions; comparisons with conventional statistical methods
Includes supplementary material: sn.pub/extras
