Overview
- Authors:
-
-
Heping Zhang
-
Department of Epidemiology and Public Health School of Medicine, Yale University, New Haven, USA
-
Burton Singer
-
Office of Population Research, Princeton University, Princeton, USA
Access this book
Other ways to access
Table of contents (12 chapters)
-
-
- Heping Zhang, Burton Singer
Pages 1-6
-
- Heping Zhang, Burton Singer
Pages 7-19
-
- Heping Zhang, Burton Singer
Pages 21-27
-
- Heping Zhang, Burton Singer
Pages 29-59
-
- Heping Zhang, Burton Singer
Pages 61-69
-
- Heping Zhang, Burton Singer
Pages 71-77
-
- Heping Zhang, Burton Singer
Pages 79-92
-
- Heping Zhang, Burton Singer
Pages 93-103
-
- Heping Zhang, Burton Singer
Pages 105-135
-
- Heping Zhang, Burton Singer
Pages 137-172
-
- Heping Zhang, Burton Singer
Pages 173-199
-
- Heping Zhang, Burton Singer
Pages 201-209
-
Back Matter
Pages 211-226
About this book
Multiple complex pathways, characterized by interrelated events and con ditions, represent routes to many illnesses, diseases, and ultimately death. Although there are substantial data and plausibility arguments supporting many conditions as contributory components of pathways to illness and disease end points, we have, historically, lacked an effective methodology for identifying the structure of the full pathways. Regression methods, with strong linearity assumptions and data-based constraints on the extent and order of interaction terms, have traditionally been the strategies of choice for relating outcomes to potentially complex explanatory pathways. How ever, nonlinear relationships among candidate explanatory variables are a generic feature that must be dealt with in any characterization of how health outcomes come about. Thus, the purpose of this book is to demon strate the effectiveness of a relatively recently developed methodology recursive partitioning-as a response to this challenge. We also compare and contrast what is learned via recursive partitioning with results ob tained 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 re gression techniques. This book is suitable for three broad groups of readers: (1) biomedical re searchers, clinicians, public health practitioners including epidemiologists, health service researchers, environmental policy advisers; (2) consulting statisticians who can use the recursive partitioning technique as a guide in providing effective and insightful solutions to clients' problems; and (3) statisticians interested in methodological and theoretical issues.
Reviews
STATISTICAL METHODS IN MEDICAL RESEARCH
"The beauty of the Zhang and Singer’s book is that it gives an excellent comparison between conventional regression models and recursive partitioning techniques. This comparative approach gives the reader insight into how a recursive partitioning technique can have an advantage over the conventional methods…Overall, the book provides an excellent introduction to tree based methods and their applications. It can be a good place to start learning about recursive partitioning. In addition, biostatisticians will enjoy the real life examples that have been used in the book."
Authors and Affiliations
-
Department of Epidemiology and Public Health School of Medicine, Yale University, New Haven, USA
Heping Zhang
-
Office of Population Research, Princeton University, Princeton, USA
Burton Singer
