Recursive Partitioning and Applications

1. Front Matter

   Pages I-XIV

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2. Introduction

   • Heping Zhang, Burton H. Singer

   Pages 1-8

3. A Practical Guide to Tree Construction

   • Heping Zhang, Burton H. Singer

   Pages 9-22

4. Logistic Regression

   • Heping Zhang, Burton H. Singer

   Pages 23-29

5. Classification Trees for a Binary Response

   • Heping Zhang, Burton H. Singer

   Pages 31-62

6. Examples Using Tree-Based Analysis

   • Heping Zhang, Burton H. Singer

   Pages 63-77

7. Random and Deterministic Forests

   • Heping Zhang, Burton H. Singer

   Pages 79-95

8. Analysis of Censored Data: Examples

   • Heping Zhang, Burton H. Singer

   Pages 97-103

9. Analysis of Censored Data: Concepts and Classical Methods

   • Heping Zhang, Burton H. Singer

   Pages 105-118

10. Analysis of Censored Data: Survival Trees and Random Forests

    • Heping Zhang, Burton H. Singer

    Pages 119-131

11. Regression Trees and Adaptive Splines for a Continuous Response

    • Heping Zhang, Burton H. Singer

    Pages 133-162

12. Analysis of Longitudinal Data

    • Heping Zhang, Burton H. Singer

    Pages 163-198

13. Analysis of Multiple Discrete Responses

    • Heping Zhang, Burton H. Singer

    Pages 199-225

14. Appendix

    • Heping Zhang, Burton H. Singer

    Pages 227-235

15. Back Matter

    Pages 237-259

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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.

• Logistic Regression
• Practical Computational Methods
• Recursive Partitioning
• Tree-based Survival Analysis
• Trees and Associated Forests
• adIOMEDICIaptive Splines and Regression Trees
• calculus
• classification
• epidemiology

• Dept. Biostatistics, Yale School of Public Health, New Haven, USA

  Heping Zhang

• Emerging Pathogens Institute, University of Florida, Gainesville, USA

  Burton H. Singer

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.

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