Overview
- Authors:
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Heping Zhang
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Dept. Biostatistics, Yale School of Public Health, New Haven, USA
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Burton H. Singer
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Emerging Pathogens Institute, University of Florida, Gainesville, USA
- 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
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Table of contents (13 chapters)
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- Heping Zhang, Burton H. Singer
Pages 1-8
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- Heping Zhang, Burton H. Singer
Pages 9-22
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- Heping Zhang, Burton H. Singer
Pages 23-29
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- Heping Zhang, Burton H. Singer
Pages 31-62
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- Heping Zhang, Burton H. Singer
Pages 63-77
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- Heping Zhang, Burton H. Singer
Pages 79-95
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- Heping Zhang, Burton H. Singer
Pages 97-103
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- Heping Zhang, Burton H. Singer
Pages 105-118
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- Heping Zhang, Burton H. Singer
Pages 119-131
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- Heping Zhang, Burton H. Singer
Pages 133-162
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- Heping Zhang, Burton H. Singer
Pages 163-198
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- Heping Zhang, Burton H. Singer
Pages 199-225
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- Heping Zhang, Burton H. Singer
Pages 227-235
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Back Matter
Pages 237-259
About this book
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.
Authors and Affiliations
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Dept. Biostatistics, Yale School of Public Health, New Haven, USA
Heping Zhang
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Emerging Pathogens Institute, University of Florida, Gainesville, USA
Burton H. Singer
About the authors
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.
