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＜電子ブック＞
Recursive Partitioning in the Health Sciences / by Heping Zhang, Burton H. Singer
(Statistics for Biology and Health. ISSN:21975671)

版	1st ed. 1999.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	1999
本文言語	英語
大きさ	XII, 226 p. 47 illus : online resource
著者標目	*Zhang, Heping author Singer, Burton H author SpringerLink (Online service)
件　名	LCSH:Life sciences LCSH:Biometry FREE:Life Sciences FREE:Biostatistics
一般注記	1 Introduction -- 2 A Practical Guide to Tree Construction -- 3 Logistic Regression -- 4 Classification Trees for a Binary Response -- 5 Risk-Factor Analysis Using Tree-Based Stratification -- 6 Analysis of Censored Data: Examples -- 7 Analysis of Censored Data: Concepts and Classical Methods -- 8 Analysis of Censored Data: Survival Trees -- 9 Regression Trees and Adaptive Splines for a Continuous Response -- 10 Analysis of Longitudinal Data -- 11 Analysis of Multiple Discrete Responses -- 12 Appendix -- References 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 HTTP:URL=https://doi.org/10.1007/978-1-4757-3027-2

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