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Unified Methods for Censored Longitudinal Data and Causality

  • Book
  • © 2003

Overview

Authors:
  1. Mark J. Laan

    1. Department of Biostatistics, University of California, Berkeley, USA

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    You can also search for this author in PubMed   Google Scholar

  2. James M. Robins

    1. Department of Epidemiology, Harvard School of Public Health, Boston, USA

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    You can also search for this author in PubMed   Google Scholar

Part of the book series: Springer Series in Statistics (SSS)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-7
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  2. Introduction

    • Mark J. van der Laan, James M. Robins
    Pages 8-101

  3. General Methodology

    • Mark J. van der Laan, James M. Robins
    Pages 102-171

  4. Monotone Censored Data

    • Mark J. van der Laan, James M. Robins
    Pages 172-231

  5. Cross-Sectional Data and Right-Censored Data Combined

    • Mark J. van der Laan, James M. Robins
    Pages 232-265

  6. Multivariate Right-Censored Multivariate Data

    • Mark J. van der Laan, James M. Robins
    Pages 266-310

  7. Unified Approach for Causal Inference and Censored Data

    • Mark J. van der Laan, James M. Robins
    Pages 311-370

  8. Back Matter

    Pages 371-399
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Keywords

About this book

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. 

Reviews

From the reviews:

"This book provides a rigourous statistical framework for the analysis of complex large longitudinal data. It provides a comprehensive description of optimal estimation techniques based on time-dependent data structures … . This is an excellent book for Ph.D. level students in Biostatistics and Statistics who have a strong background in mathematics. It is also suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data." (Subhash C. Kochar, Sankhya: The Indian Journal of Statistics, Vol. 66 (1), 2004)

"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 … . The book can be used to teach masters-level 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." (P. Rochus, Mathematical Reviews, 2003m)

"This book by two major research workers in the field addresses in generality important problems involving multivariate longitudinal data … . it is an important book dealing with important problems. Therefore, experts in modern semi-parametric theory should certainly read the book. Those with an interest focussed more on applications and able to draw together a reading group with appropriate expertise are very likely to profit greatly from a sustained study of the book." (D.R. Cox, Short Book Reviews, Vol. 23 (2), 2003)

Authors and Affiliations

  • Department of Biostatistics, University of California, Berkeley, USA

    Mark J. Laan

  • Department of Epidemiology, Harvard School of Public Health, Boston, USA

    James M. Robins

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