| Description |
1 online resource (xvi, 204 pages) : illustrations |
| Series |
Statistics for Biology and Health, 1431-8776 |
|
Statistics for biology and health, 1431-8776
|
| Contents |
The Data: Observational Studies and Sequentially Randomized Trials -- Statistical Reinforcement Learning -- Estimation of Optimal DTRs by Modeling Contrasts of Conditional Mean Outcomes -- Estimation of Optimal DTRs by Directly Modeling Regimes -- G-computation: Parametric Estimation of Optimal DTRs -- Estimation DTRs for Alternative Outcome Types -- Inference and Non-regularity -- Additional Considerations and Final Thoughts |
| Summary |
Statistical Methods for Dynamic Treatment Regimes shares state of the art of statistical methods developed to address questions of estimation and inference for dynamic treatment regimes, a branch of personalized medicine. This volume demonstrates these methods with their conceptual underpinnings and illustration through analysis of real and simulated data. These methods are immediately applicable to the practice of personalized medicine, which is a medical paradigm that emphasizes the systematic use of individual patient information to optimize patient health care. This is the first single source to provide an overview of methodology and results gathered from journals, proceedings, and technical reports with the goal of orienting researchers to the field. The first chapter establishes context for the statistical reader in the landscape of personalized medicine. Readers need only have familiarity with elementary calculus, linear algebra, and basic large-sample theory to use this text. Throughout the text, authors direct readers to available code or packages in different statistical languages to facilitate implementation. In cases where code does not already exist, the authors provide analytic approaches in sufficient detail that any researcher with knowledge of statistical programming could implement the methods from scratch. This will be an important volume for a wide range of researchers, including statisticians, epidemiologists, medical researchers, and machine learning researchers interested in medical applications. Advanced graduate students in statistics and biostatistics will also find material in Statistical Methods for Dynamic Treatment Regimes to be a critical part of their studies |
| Bibliography |
Includes bibliographical references and index |
| Notes |
English |
|
Online resource; title from PDF title page (SpringerLink, viewed August 20, 2013) |
| Subject |
Medical statistics.
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Medical records -- Data processing.
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Decision making -- Mathematical models.
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Evidence-based medicine.
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Cohort analysis.
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Medical personnel.
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Longitudinal method.
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Statistics.
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Methodology.
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Decision Support Techniques
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Evidence-Based Practice
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Epidemiologic Methods
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Cohort Studies
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Investigative Techniques
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Epidemiologic Studies
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Health Occupations
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Analytical, Diagnostic and Therapeutic Techniques and Equipment
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Disciplines and Occupations
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Epidemiologic Study Characteristics as Topic
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Evidence-Based Medicine
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Longitudinal Studies
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Models, Statistical
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Statistics as Topic
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Methods
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Data Interpretation, Statistical
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methodology.
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statistics.
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Historias clínicas -- Datos-Tratamiento
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Toma de decisiones -- Modelos matemáticos
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Medicina basada en la evidencia
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Estadísticas médicas
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Salud pública-Personal
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Statistics
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Methodology
|
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Medical personnel
|
|
Longitudinal method
|
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Evidence-based medicine
|
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Decision making -- Mathematical models
|
|
Cohort analysis
|
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Medical records -- Data processing
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Medical statistics
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| Genre/Form |
Medical statistics
|
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Medical statistics.
|
| Form |
Electronic book
|
| Author |
Moodie, Erica E. M., author
|
| ISBN |
9781461474289 |
|
1461474280 |
|
1461474272 |
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9781461474272 |
|
