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Unified Methods for Censored Longitudinal Data and Causality
Taschenbuch von James M Robins (u. a.)
Sprache: Englisch

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Beschreibung
During the last decades, there has been an explosion in computation and information technology. This development comes with an expansion of complex observational studies and clinical trials in a variety of fields such as medicine, biology, epidemiology, sociology, and economics among many others, which involve collection of large amounts of data on subjects or organisms over time. The goal of such studies can be formulated as estimation of a finite dimensional parameter of the population distribution corresponding to the observed time- dependent process. Such estimation problems arise in survival analysis, causal inference and regression analysis. This book provides a fundamental statistical framework for the analysis of complex longitudinal data. It provides the first comprehensive description of optimal estimation techniques based on time-dependent data structures subject to informative censoring and treatment assignment in so called semiparametric models. Semiparametric models are particularly attractive since they allow the presence of large unmodeled nuisance parameters. These techniques include estimation of regression parameters in the familiar (multivariate) generalized linear regression and multiplicative intensity models. They go beyond standard statistical approaches by incorporating all the observed data to allow for informative censoring, to obtain maximal efficiency, and by developing estimators of causal effects. It can be used to teach masters and Ph.D. students in biostatistics and statistics and is suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data.
During the last decades, there has been an explosion in computation and information technology. This development comes with an expansion of complex observational studies and clinical trials in a variety of fields such as medicine, biology, epidemiology, sociology, and economics among many others, which involve collection of large amounts of data on subjects or organisms over time. The goal of such studies can be formulated as estimation of a finite dimensional parameter of the population distribution corresponding to the observed time- dependent process. Such estimation problems arise in survival analysis, causal inference and regression analysis. This book provides a fundamental statistical framework for the analysis of complex longitudinal data. It provides the first comprehensive description of optimal estimation techniques based on time-dependent data structures subject to informative censoring and treatment assignment in so called semiparametric models. Semiparametric models are particularly attractive since they allow the presence of large unmodeled nuisance parameters. These techniques include estimation of regression parameters in the familiar (multivariate) generalized linear regression and multiplicative intensity models. They go beyond standard statistical approaches by incorporating all the observed data to allow for informative censoring, to obtain maximal efficiency, and by developing estimators of causal effects. It can be used to teach masters and Ph.D. students in biostatistics and statistics and is suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data.
Zusammenfassung
During the last decades, there has been an explosion in computation and information technology. This development comes with an expansion of complex observational studies and clinical trials in a variety of fields such as medicine, biology, epidemiology, sociology, and economics among many others, which involve collection of large amounts of data on subjects or organisms over time. This book provides a fundamental statistical framework for the analysis of this type of data.
Inhaltsverzeichnis
Introduction * General Methodology * Monotone Censored Data * Cross Sectional Data and Right Censored Data Combined * Multivariate Right Censored Multivariate Data * Unified Approach for Causal Inference and Censored Data
Details
Erscheinungsjahr: 2011
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Importe, Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Inhalt: xii
399 S.
2 s/w Illustr.
399 p. 2 illus.
ISBN-13: 9781441930552
ISBN-10: 1441930558
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Robins, James M
Laan, Mark J. Van Der
Auflage: Softcover reprint of hardcover 1st edition 2003
Hersteller: Springer US
Springer New York
Springer US, New York, N.Y.
Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Maße: 235 x 155 x 23 mm
Von/Mit: James M Robins (u. a.)
Erscheinungsdatum: 26.05.2011
Gewicht: 0,622 kg
Artikel-ID: 107252908
Zusammenfassung
During the last decades, there has been an explosion in computation and information technology. This development comes with an expansion of complex observational studies and clinical trials in a variety of fields such as medicine, biology, epidemiology, sociology, and economics among many others, which involve collection of large amounts of data on subjects or organisms over time. This book provides a fundamental statistical framework for the analysis of this type of data.
Inhaltsverzeichnis
Introduction * General Methodology * Monotone Censored Data * Cross Sectional Data and Right Censored Data Combined * Multivariate Right Censored Multivariate Data * Unified Approach for Causal Inference and Censored Data
Details
Erscheinungsjahr: 2011
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Importe, Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Inhalt: xii
399 S.
2 s/w Illustr.
399 p. 2 illus.
ISBN-13: 9781441930552
ISBN-10: 1441930558
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Robins, James M
Laan, Mark J. Van Der
Auflage: Softcover reprint of hardcover 1st edition 2003
Hersteller: Springer US
Springer New York
Springer US, New York, N.Y.
Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Maße: 235 x 155 x 23 mm
Von/Mit: James M Robins (u. a.)
Erscheinungsdatum: 26.05.2011
Gewicht: 0,622 kg
Artikel-ID: 107252908
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