Unified Methods for Censored Longitudinal Data and Causality

Name: Unified Methods for Censored Longitudinal Data and Causality
Brand: Springer-Verlag New York Inc.
SKU: 9780387955568
Price: 892.45 RON
Availability: InStock

Autor: M. J. Laan, J. Robins

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Legare: Copertă tare

Editura: Springer-Verlag New York Inc.

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During the last decades, there has been an explosion in computation and information technology. This...

Informații despre carte

Autor

M. J. Laan, J. Robins

Limbă

engleză

Legare

Carte - Copertă tare

Publicat

2003

Pagini

399

EAN

9780387955568

ISBN

0387955569

Enbook ID

01384461

Editura

Springer-Verlag New York Inc.

Greutate

817

Dimensiuni

155 x 235 x 25

Descriere completă

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.

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