Skip to content

Online ordering will be unavailable on Saturday 10 December 2022, 0800-1800 GMT.

To place an order, please contact Customer Services.

UK/ROW directcs@cambridge.org +44 (0) 1223 326050 | US customer_service@cambridge.org 1 800 872 7423 or 1 212 337 5000 | Australia/New Zealand enquiries@cambridge.edu.au 61 3 86711400 or 1800 005 210, New Zealand 0800 023 520

Register Sign in Wishlist
Adaptive Treatment Strategies in Practice

Adaptive Treatment Strategies in Practice
Planning Trials and Analyzing Data for Personalized Medicine

c.$79.99 ( )

Part of ASA-SIAM Series on Statistics and Applied Probability

M. R. Kosorok, E. E. M. Moodie, K. M. Kidwell, R. Dawson, P. W. Lavori, P. F. Thall, P. Ghosh, Y. K. Cheung, B. Chakraborty, M. P. Wallace, D. A. Stephens, Y. Q. Zhao, M. Davidian, A. A. Tsiatis, E. B. Laber, M. Petersen, J. Schwab, E. Geng, M. J. van der Laan, S. M. Shortreed, J. Pineau, S. A. Murphy, G. Chen, D. Zeng, G. S. Johnson, A. Topp, A. S. Wahed, M. Yuan, K. A. Linn, L. A. Stefanski, R. D. Vincent, N. Ybarra, I. El Naqa, Y. F. Zhao
View all contributors
  • Date Published: October 2016
  • availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
  • format: Paperback
  • isbn: 9781611974171

Paperback

Add to wishlist

Looking for an examination copy?

This title is not currently available for examination. However, if you are interested in the title for your course we can consider offering an examination copy. To register your interest please contact collegesales@cambridge.org providing details of the course you are teaching.

Description
Product filter button
Description
Contents
Resources
Courses
About the Authors
  • Personalized medicine is a medical paradigm that emphasizes systematic use of individual patient information to optimize that patient's health care, particularly in managing chronic conditions and treating cancer. In the statistical literature, sequential decision making is known as an adaptive treatment strategy (ATS) or a dynamic treatment regime (DTR). The field of DTRs emerges at the interface of statistics, machine learning and biomedical science to provide a data-driven framework for precision medicine. A learning-by-seeing approach to the development of ATSs is provided in this book. While estimation procedures are described in sufficient heuristic and technical detail, so that less quantitative readers can understand the broad principles underlying the approaches, practices can also be implemented by more quantitative readers. As the most up-to-date summary of the current state of statistical research in personalized medicine, this book is ideal for a broad audience of health researchers.

    • Provides the most up-to-date summary of the current state of the statistical research in personalized medicine
    • Contains chapters by leaders in the area from both the statistics and computer sciences fields
    • Contains a range of practical advice, introductory and expository materials, and case studies
    Read more

    Customer reviews

    Not yet reviewed

    Be the first to review

    Review was not posted due to profanity

    ×

    , create a review

    (If you're not , sign out)

    Please enter the right captcha value
    Please enter a star rating.
    Your review must be a minimum of 12 words.

    How do you rate this item?

    ×

    Product details

    • Date Published: October 2016
    • format: Paperback
    • isbn: 9781611974171
    • length: 364 pages
    • dimensions: 250 x 177 x 23 mm
    • weight: 0.76kg
    • availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
  • Table of Contents

    List of contributors
    List of figures
    Preface
    1. Introduction M. R. Kosorok and E. E. M. Moodie
    Part I. Design of Trials for Estimating Dynamic Treatment Regimes:
    2. DTRs and SMARTs: definitions, designs, and applications K. M. Kidwell
    3. Efficient design for clinically relevant intent-to-treat comparisons R. Dawson and P. W. Lavori
    4. SMART design, conduct, and analysis in oncology P. F. Thall
    5. Sample size calculations for clustered SMART designs P. Ghosh, Y. K. Cheung and B. Chakraborty
    Part II. Practical Challenges in Dynamic Treatment Regime Analyses:
    6. Analysis in the single-stage setting: an overview of estimation approaches for dynamic treatment regimes M. P. Wallace and E. E. M. Moodie
    7. G-estimation for dynamic treatment regimes in the longitudinal setting D. A. Stephens
    8. Outcome weighted learning methods for optimal dynamic treatment regimes Y. Q. Zhao
    9. Value search estimators for optimal dynamic treatment regimes M. Davidian, A. A. Tsiatis and E. B. Laber
    10. Evaluation of longitudinal dynamics with and without marginal structural working models M. Petersen, J. Schwab, E. Geng and M. J. van der Laan
    11. Imputation strategy for SMARTs S. M. Shortreed, E. B. Laber, J. Pineau and S. A. Murphy
    12. Clinical trials for personalized dose finding G. Chen and D. Zeng
    13. Methods for analyzing DTRs with censored survival data G. S. Johnson, A. Topp and A. S. Wahed
    14. Outcome weighted learning with a reject option M. Yuan
    15. Estimation of dynamic treatment regimes for complex outcomes: balancing benefits and risks K. A. Linn, E. B. Laber and L. A. Stefanski
    16. Practical reinforcement learning in dynamic treatment regimes R. D. Vincent, J. Pineau, N. Ybarra and I. El Naqa
    17. Reinforcement learning applications in clinical trials Y. F. Zhao
    Bibliography
    Index.

  • Authors

    Michael R. Kosorok, University of North Carolina, Chapel Hill
    Michael R. Kosorok is W. R. Kenan, Jr Distinguished Professor and Chair of Biostatistics and Professor of Statistics and Operations Research at the University of North Carolina, Chapel Hill. He is an honorary fellow of both the American Statistical Association and the Institute of Mathematical Statistics and an Associate Editor of The Annals of Statistics, the Journal of the American Statistical Association, and the Journal of the Royal Statistical Society, Series B. He is the contact principal investigator for a program project (P01) from the US National Cancer Institute, entitled 'Statistical Methods for Cancer Clinical Trials'. His main research interests are in precision medicine, clinical trials, machine learning, and related areas.

    Erica E. M. Moodie, McGill University, Montréal
    Erica E. M. Moodie is a William Dawson Scholar and an Associate Professor of Biostatistics in the Department of Epidemiology, Biostatistics and Occupational Health at McGill University. She is an Elected Member of the International Statistical Institute, and an Associate Editor of Biometrics and the Journal of the American Statistical Association. She holds a Chercheur–Boursier Junior 2 career award from the Fonds de Recherche du Québec-Santé. Her main research interests are in causal inference and longitudinal data, with a focus on dynamic treatment regimes.

    Contributors

    M. R. Kosorok, E. E. M. Moodie, K. M. Kidwell, R. Dawson, P. W. Lavori, P. F. Thall, P. Ghosh, Y. K. Cheung, B. Chakraborty, M. P. Wallace, D. A. Stephens, Y. Q. Zhao, M. Davidian, A. A. Tsiatis, E. B. Laber, M. Petersen, J. Schwab, E. Geng, M. J. van der Laan, S. M. Shortreed, J. Pineau, S. A. Murphy, G. Chen, D. Zeng, G. S. Johnson, A. Topp, A. S. Wahed, M. Yuan, K. A. Linn, L. A. Stefanski, R. D. Vincent, N. Ybarra, I. El Naqa, Y. F. Zhao

Sorry, this resource is locked

Please register or sign in to request access. If you are having problems accessing these resources please email lecturers@cambridge.org

Register Sign in
Please note that this file is password protected. You will be asked to input your password on the next screen.

» Proceed

You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.

Continue ×

Continue ×

Continue ×
warning icon

Turn stock notifications on?

You must be signed in to your Cambridge account to turn product stock notifications on or off.

Sign in Create a Cambridge account arrow icon
×

Find content that relates to you

Join us online

This site uses cookies to improve your experience. Read more Close

Are you sure you want to delete your account?

This cannot be undone.

Cancel

Thank you for your feedback which will help us improve our service.

If you requested a response, we will make sure to get back to you shortly.

×
Please fill in the required fields in your feedback submission.
×