Recursive Partitioning in the Health Sciences:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
1999
|
| Schriftenreihe: | Statistics for Biology and Health
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | 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 |
| Beschreibung: | 1 Online-Ressource (XII, 226 p) |
| ISBN: | 9781475730272 9781475730296 |
| ISSN: | 1431-8776 |
| DOI: | 10.1007/978-1-4757-3027-2 |
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| dewey-full | 519.5 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5 |
| dewey-search | 519.5 |
| dewey-sort | 3519.5 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1007/978-1-4757-3027-2 |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:21:08Z |
| institution | BVB |
| isbn | 9781475730272 9781475730296 |
| issn | 1431-8776 |
| language | English |
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| spelling | Zhang, Heping Verfasser aut Recursive Partitioning in the Health Sciences by Heping Zhang, Burton Singer New York, NY Springer New York 1999 1 Online-Ressource (XII, 226 p) txt rdacontent c rdamedia cr rdacarrier Statistics for Biology and Health 1431-8776 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 Statistics Statistics for Life Sciences, Medicine, Health Sciences Statistik Medizinische Statistik (DE-588)4127563-9 gnd rswk-swf Medizinische Statistik (DE-588)4127563-9 s 1\p DE-604 Singer, Burton Sonstige oth https://doi.org/10.1007/978-1-4757-3027-2 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Zhang, Heping Recursive Partitioning in the Health Sciences Statistics Statistics for Life Sciences, Medicine, Health Sciences Statistik Medizinische Statistik (DE-588)4127563-9 gnd |
| subject_GND | (DE-588)4127563-9 |
| title | Recursive Partitioning in the Health Sciences |
| title_auth | Recursive Partitioning in the Health Sciences |
| title_exact_search | Recursive Partitioning in the Health Sciences |
| title_full | Recursive Partitioning in the Health Sciences by Heping Zhang, Burton Singer |
| title_fullStr | Recursive Partitioning in the Health Sciences by Heping Zhang, Burton Singer |
| title_full_unstemmed | Recursive Partitioning in the Health Sciences by Heping Zhang, Burton Singer |
| title_short | Recursive Partitioning in the Health Sciences |
| title_sort | recursive partitioning in the health sciences |
| topic | Statistics Statistics for Life Sciences, Medicine, Health Sciences Statistik Medizinische Statistik (DE-588)4127563-9 gnd |
| topic_facet | Statistics Statistics for Life Sciences, Medicine, Health Sciences Statistik Medizinische Statistik |
| url | https://doi.org/10.1007/978-1-4757-3027-2 |
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