Adaptive Treatment Strategies in Practice
Planning Trials and Analyzing Data for Personalized Medicine
Part of ASA-SIAM Series on Statistics and Applied Probability
- Authors:
- Michael R. Kosorok, University of North Carolina, Chapel Hill
- Erica E. M. Moodie, McGill University, Montréal
- Date Published: January 2016
- availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial null Mathematics for availability.
- format: Paperback
- isbn: 9781611974171
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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.
Read more- 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
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×Product details
- Date Published: January 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 null 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.
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