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Recursive Partitioning in the Health Sciences

  • Book
  • © 1999


Part of the book series: Statistics for Biology and Health (SBH)

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Table of contents (12 chapters)


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.



"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

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