Daniel Commenges, Helene Jacqmin-Gadda

Dynamical Biostatistical Models (eBook, PDF)

• Format: PDF

Produktbeschreibung

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

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 408
• Erscheinungstermin: 2. Oktober 2015
• Englisch
• ISBN-13: 9781498729680
• Artikelnr.: 43973723

Autorenporträt

Daniel Commenges is emeritus research director at INSERM and founder of the Biostatistics Team at the University of Bordeaux. Dr. Commenges has published more than 200 papers and was editor of Biometrics and an associate editor of several other journals. His main research interests focus on statistical models in epidemiology and biology, applications of stochastic processes, statistical inference in dynamical models, and model selection.

Helene Jacqmin-Gadda is research director at INSERM and head of the Biostatistics Team at the University of Bordeaux. Dr. Jacqmin-Gadda is a member of the International Biometrics Society and was an associate editor of Biometrics. Her research involves methods for analyzing longitudinal data and joint models in areas, including brain aging, HIV, and cancer.

Inhaltsangabe

Introduction. Classical Biostatistical Models: Inference. Survival
Analysis. Models for Longitudinal Data. Advanced Biostatistical Models:
Extensions of Mixed Models. Advanced Survival Models. Multistate Models.
Joint Models for Longitudinal and Time-to-Event Data. The Dynamic Approach
to Causality. Appendix: Software. Index.