Statistical methods for dynamic treatment regimes : reinforcement learning, causal inference, and personalized medicine
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...
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| Hlavní autoři: | , |
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| Médium: | Livre numérique |
| Jazyk: | Anglais |
| Vydáno: |
New York, NY :
Springer New York
2013.
Cham : Springer Nature |
| Edice: | Statistics for Biology and Health
76 |
| On-line přístup: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Poznámka: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Statistical methods for dynamic treatment regimes, reinforcement learning, causal inference, and personalized medicine, Bibhas Chakraborty, Erica E.M. Moodie, 2013, New York, Springer, 1 vol. (XVI-204 p.), Statistics for Biology and Health, 978-1-461-47427-2 • Statistical Methods for Dynamic Treatment Regimes, Texte imprimé, 9781461474296 • Statistical methods for dynamic treatment regimes, reinforcement learning, causal inference, and personalized medicine, Bibhas Chakraborty, Erica E.M. Moodie, 2013, New York, Springer, 1 vol. (XVI-204 p.), Statistics for Biology and Health, 978-1-461-47427-2 |
Obsah:
- Introduction
- 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
- Glossary
- Index
- References.