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Dynamical biostatistical models / Daniel Commenges and Helene Jacqmin-Gadda.

By: Contributor(s): Series: Chapman & Hall/CRC biostatistics seriesPublication details: Boca Raton : CRC Press, ©2016.Description: xxxiv, 374 p. : ill. ; 25 cmISBN:
  • 9781498729673
Subject(s): DDC classification:
  • 000SB:614.4 23 C734
Contents:
1. Introduction -- 2. Inference -- 3. Survival analysis -- 4. Models for longitudinal data -- 5. Extensions of mixed models -- 6. Advanced survival models -- 7. Multistate models -- 8. Joint models for longitudinal and time-to-event data -- 9. The dynamic approach to causality -- 10. Appendix: software.
Summary: This book presents statistical models and methods for the analysis of longitudinal data. The book focuses on models for analyzing repeated measures of quantitative and qualitative variables and events history, including survival and multistate models. Most of the advanced methods, such as multistate and joint models, can be applied using SAS or R software. It describes advanced regression models that include the time dimension, such as mixed-effect models, survival models, multistate models, and joint models for repeated measures and time-to-event data. It also explores the possibility of unifying these models through a stochastic process point of view and introduces the dynamic approach to causal inference.
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Includes bibliographical references and index.

1. Introduction --
2. Inference --
3. Survival analysis --
4. Models for longitudinal data --
5. Extensions of mixed models --
6. Advanced survival models --
7. Multistate models --
8. Joint models for longitudinal and time-to-event data --
9. The dynamic approach to causality --
10. Appendix: software.

This book presents statistical models and methods for the analysis of longitudinal data. The book focuses on models for analyzing repeated measures of quantitative and qualitative variables and events history, including survival and multistate models. Most of the advanced methods, such as multistate and joint models, can be applied using SAS or R software.
It describes advanced regression models that include the time dimension, such as mixed-effect models, survival models, multistate models, and joint models for repeated measures and time-to-event data. It also explores the possibility of unifying these models through a stochastic process point of view and introduces the dynamic approach to causal inference.

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