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... illustration through analysis of real and simulated data. These methods are immediately applicable to the practice of personalized medicine, which is a medical paradigm that emphasizes the systematic use of individual patient information to optimize patient health care. This is the first single source to provide an overview of methodology and results gathered from journals, proceedings, and technical reports with the goal of orienting researchers to the field. The first chapter establishes context for the statistical reader in the landscape of personalized medicine. Readers need only have familiarity with elementary calculus, linear algebra, and basic large-sample theory to use this text. Throughout the text, authors direct readers to available code or packages in different statistical languages to facilitate implementation. In cases where code does not already exist, the authors provide analytic approaches in sufficient detail that any researcher with knowledge of statistical program Ausführliche Beschreibung Beschreibung ausblenden

1. Person:	Chakraborty, Bibhas
Weitere Körperschaften:	SpringerLink (Online service)
Weitere Personen:	Moodie, Erica E.M. [author]
Format:	E-Buch
Sprache:	English
Veröffentlicht:	New York, NY Springer New York 2013, 2013
Beschreibung:	XVI, 204 p online resource
Ausgabe:	1st ed. 2013
Serien:	Statistics for Biology and Health
Schlagworte:	Statistics for Life Sciences, Medicine, Health Sciences Health Informatics Medical records / Data processing Statistics, general Statistics
Online Zugang:	Volltext
Tags:	Hinzufügen Keine Tags. Fügen Sie den ersten Tag hinzu!