Recursive Partitioning in the Health Sciences / by Heping Zhang, Burton Singer
(Statistics for Biology and Health)
| データ種別 | 電子ブック |
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| 出版情報 | New York, NY : Springer New York : Imprint: Springer , 1999 |
| 本文言語 | 英語 |
| 大きさ | XII, 226 p. 47 illus : online resource |
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| 内容注記 | 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 |
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| 一般注記 | 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 |
| 著者標目 | *Zhang, Heping author Singer, Burton author SpringerLink (Online service) |
| 件 名 | LCSH:Life sciences LCSH:Statistics FREE:Life Sciences FREE:Life Sciences, general FREE:Statistics for Life Sciences, Medicine, Health Sciences |
| 分 類 | DC23:570 |
| 巻冊次 | ISBN:9781475730272 
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| ISBN | 9781475730272 |
| URL | http://dx.doi.org/10.1007/978-1-4757-3027-2 |
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