Recursive Partitioning and Applications
The routes to many important outcomes including diseases and ultimately death as well as financial credit consist of multiple complex pathways containing interrelated events and conditions. We have historically lacked effective methodologies for identifyi
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Heping Zhang • Burton H. Singer
Recursive Partitioning and Applications Second Edition
Heping Zhang Department of Epidemiology and Public Health Yale University School of Medicine 60 College Street New Haven, Connecticut 06520-8034 USA [email protected]
Burton H. Singer Emerging Pathogens Institute University of Florida PO Box 100009 Gainesville, FL 32610 USA
ISSN 0172-7397 e-ISBN 978-1-4419-6824-1 ISBN 978-1-4419-6823-4 DOI 10.1007/978-1-4419-6824-1 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2010930849 c Springer Science+Business Media, LLC 2010 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Dedicated to Julan, Jeffrey, and Leon (HZ) and to Eugenia, Gregory, Maureen, and Sheila (BS)
Preface
Multiple complex pathways, characterized by interrelated events and conditions, 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. 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 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 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. This book is a revis
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