Targeted Learning: Casual Inference for Observational and Experimental Data

van der Laan, Mark J.; Sherri Rose

ISBN 10: 1441997814 / ISBN 13: 9781441997814

Verlag: Springer Publishing Company, New York, 2011

Beschreibung

Light wear to boards, corners bumped. Pages appear free of writing / highlighting. In nice shape.

Bestandsnummer des Verkäufers 6807

Bibliografische Details

• Titel: Targeted Learning: Casual Inference for Observational and Experimental Data
• Verlag: Springer Publishing Company, New York
• Erscheinungsjahr: 2011
• Sprache: Englisch
• ISBN-10: 1441997814
• ISBN-13: 9781441997814
• Einband: Hardcover, 626 pages
• Zustand: Very Good

Über diesen Titel

Inhaltsangabe

Models, Inference, and Truth.- The Open Problem.- Defining the Model and Parameter.- Super Learning.- Introduction to TMLE.- Understanding TMLE.- Why TMLE?.- Bounded Continuous Outcomes.- Direct Effects and Effect Among the Treated.- Marginal Structural Models.- Positivity.- Robust Analysis of RCTs Using Generalized Linear Models.- Targeted ANCOVA Estimator in RCTs.- Independent Case-Control Studies.- Why Match? Matched Case-Control Studies.- Nested Case-Control Risk Score Prediction.- Super Learning for Right-Censored Data.- RCTs with Time-to-Event Outcomes.- RCTs with Time-to-Event Outcomes and Effect Modification Parameters.- C-TMLE of an Additive Point Treatment Effect.- C-TMLE for Time-to-Event Outcomes.- Propensity-Score-Based Estimators and C-TMLE.- Targeted Methods for Biomarker Discovery.- Finding Quantitative Trait Loci Genes.- Case Study: Longitudinal HIV Cohort Data.- Probability of Success of an In Vitro Fertilization Program.- Individualized Antiretroviral Initiation Rules.- Cross-Validated Targeted Minimum-Loss-Based Estimation.- Targeted Bayesian Learning.- TMLE in Adaptive Group Sequential Covariate Adjusted RCTs.- Foundations of TMLE.- Introduction to R Code Implementation.

Über die Autorin bzw. den Autor

Mark J. van der Laan is a Hsu/Peace Professor of Biostatistics and Statistics at the University of California, Berkeley.  His research concerns causal inference, prediction, adjusting for missing and censored data, and estimation based on high-dimensional observational and experimental biomedical and genomic data.  He is the recipient of the 2005 COPSS Presidents’ and Snedecor Awards, as well as the 2004 Spiegelman Award, and is a Founding Editor for the International Journal of Biostatistics.

Sherri Rose is currently a PhD candidate in the Division of Biostatistics at the University of California, Berkeley.  Her research interests include causal inference, prediction, and applications in rare diseases. Upon completion of her doctoral degree, she will begin an NSF Mathematical Sciences Postdoctoral Research Fellowship at Johns Hopkins Bloomberg School of Public Health.