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Statistical Learning for Biomedical Data


Part of Practical Guides to Biostatistics and Epidemiology

  • Date Published: February 2011
  • availability: Available
  • format: Hardback
  • isbn: 9780521875806

£ 82.00

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About the Authors
  • This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random Forests™, neural nets, support vector machines, nearest neighbors and boosting.

    • Free open-source computer code is available online
    • Brings valuable new ideas from probability and computer science into the biomedical world to provide more accurate predictions
    • Plain-language approach makes the techniques more accessible
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    Reviews & endorsements

    'The book is well written and provides nice graphics and numerous applications.' Michael R. Chernick, Technometrics

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    Product details

    • Date Published: February 2011
    • format: Hardback
    • isbn: 9780521875806
    • length: 298 pages
    • dimensions: 253 x 179 x 21 mm
    • weight: 0.75kg
    • contains: 47 b/w illus. 25 tables
    • availability: Available
  • Table of Contents

    Part I. Introduction:
    1. Prologue
    2. The landscape of learning machines
    3. A mangle of machines
    4. Three examples and several machines
    Part II. A Machine Toolkit:
    5. Logistic regression
    6. A single decision tree
    7. Random forests – trees everywhere
    Part III. Analysis Fundamentals:
    8. Merely two variables
    9. More than two variables
    10. Resampling methods
    11. Error analysis and model validation
    Part IV. Machine Strategies:
    12. Ensemble methods – let's take a vote
    13. Summary and conclusions

  • Instructors have used or reviewed this title for the following courses

    • Analysis of Categorical Data
    • Applied Epidemiology
    • Fundamentals of Biostatistics
    • Statistics for Health Sciences
  • Authors

    James D. Malley, National Institutes of Health, Maryland
    James D. Malley is a Research Mathematical Statistician in the Mathematical and Statistical Computing Laboratory, Division of Computational Bioscience, Center for Information Technology, at the National Institutes of Health.

    Karen G. Malley, Malley Research Programming, Maryland
    Karen G. Malley is president of Malley Research Programming, Inc. in Rockville, Maryland, providing statistical programming services to the pharmaceutical industry and the National Institutes of Health. She also serves on the global council of the Clinical Data Interchange Standards Consortium (CDISC) user network, and the steering committee of the Washington, DC area CDISC user network.

    Sinisa Pajevic, National Institutes of Health, Maryland
    Sinisa Pajevic is a Staff Scientist in the Mathematical and Statistical Computing Laboratory, Division of Computational Bioscience, Center for Information Technology, at the National Institutes of Health.

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