Michael R Kosorok, Erica E M Moodie

Adaptive Treatment Strategies in Practice

Planning Trials and Analyzing Data for Personalized Medicine

• Broschiertes Buch

Produktbeschreibung

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. The authors provide a learning-by-seeing approach to the development of ATSs, aimed at a broad audience of health researchers. All estimation procedures used are described in sufficient heuristic and technical detail so that less quantitative readers can understand the broad principles underlying the approaches. At the same time, more quantitative readers can implement these practices. This book: 1. Provides the most up-to-date summary of the current state of the statistical research in personalized medicine. 2. Contains chapters by leaders in the area from both the statistics and computer sciences fields. 3. Contains a range of practical advice, introductory and expository materials, and case studies.

Produktdetails

• Verlag: Society for Industrial and Applied Mathematics (SIAM)
• Seitenzahl: 364
• Erscheinungstermin: 30. Dezember 2015
• Englisch
• Abmessung: 256mm x 177mm x 25mm
• Gewicht: 779g
• ISBN-13: 9781611974171
• ISBN-10: 1611974178
• Artikelnr.: 44918346

Autorenporträt

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.

Inhaltsangabe

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.