Recursive Partitioning in the Health Sciences

  • Heping Zhang
  • Burton Singer
Part of the Statistics for Biology and Health book series (SBH)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Heping Zhang, Burton Singer
    Pages 1-6
  3. Heping Zhang, Burton Singer
    Pages 7-19
  4. Heping Zhang, Burton Singer
    Pages 21-27
  5. Heping Zhang, Burton Singer
    Pages 29-59
  6. Heping Zhang, Burton Singer
    Pages 61-69
  7. Heping Zhang, Burton Singer
    Pages 71-77
  8. Heping Zhang, Burton Singer
    Pages 79-92
  9. Heping Zhang, Burton Singer
    Pages 93-103
  10. Heping Zhang, Burton Singer
    Pages 105-135
  11. Heping Zhang, Burton Singer
    Pages 137-172
  12. Heping Zhang, Burton Singer
    Pages 173-199
  13. Heping Zhang, Burton Singer
    Pages 201-209
  14. Back Matter
    Pages 211-226

About this book

Introduction

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.

Keywords

Computerassistierte Detektion Factor analysis Logistic Regression Radiologieinformationssystem Recursive Partitioning Statistics for Health Sciences classification methodology statistics

Authors and affiliations

  • Heping Zhang
    • 1
  • Burton Singer
    • 2
  1. 1.Department of Epidemiology and Public Health School of MedicineYale UniversityNew HavenUSA
  2. 2.Office of Population ResearchPrinceton UniversityPrincetonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4757-3027-2
  • Copyright Information Springer-Verlag New York 1999
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4757-3029-6
  • Online ISBN 978-1-4757-3027-2
  • Series Print ISSN 1431-8776
  • About this book