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Dynamical Biostatistical Models

Progresses from simple methods, such as standard repeated measures and survival analysis, to advanced ones, such as multistate and joint models
Links the dynamical approach to causality and mechanistic models, enabling the unification of dynamical models, the formalization of causal interpretations, and the connection between mechanistic and more descriptive models
Offers an introduction to statistical inference, including important results of maximum likelihood theory, model selection, and the main optimization algorithms
Includes detailed examples taken from brain aging, HIV infection, and cancer studies, allowing readers to see how the models answer real epidemiological questions
Describes how to implement the models and analyze the examples using SAS and R

Series: Chapman & Hall/CRC Biostatistics Series

By: Daniel Commenges (Author), Hélène Jacqmin-Gadda (Author)

408 pages, 46 b/w illustrations, 34 tables

Productivity Press

Hardback | Nov 2015 | #229587 | ISBN-13: 9781498729673
Availability: Usually dispatched within 6 days Details
NHBS Price: £57.99 $73/€69 approx

About this book

Dynamical Biostatistical Models 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.

Dynamical Biostatistical Models 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.

Drawing on much of their own extensive research, the authors use three main examples throughout the text to illustrate epidemiological questions and methodological issues. Readers will see how each method is applied to real data and how to interpret the results.


Contents

Introduction
- General presentation of the book
- Organization of the book
- Notation
- Presentation of examples

Classical Biostatistical Models
- Inference
- Generalities on inference: the concept of model
- Likelihood and applications
- Other types of likelihoods and estimation methods
- Model choice
- Optimization algorithms

Survival Analysis
- Introduction
- Event, origin, and functions of interest
- Observation patterns: censoring and truncation
- Estimation of the survival function
- The proportional hazards model
- Accelerated failure time model
- Counting processes approach
- Additive hazards models
- Degradation models

Models for Longitudinal Data
- Linear mixed models
- Generalized mixed linear models
- Non-linear mixed models
- Marginal models and generalized estimating equations (GEE)
- Incomplete longitudinal data
- Modeling strategies

Advanced Biostatistical Models
- Extensions of Mixed Models
- Mixed models for curvilinear outcomes
- Mixed models for multivariate longitudinal data
- Latent class mixed models

Advanced Survival Models
- Relative survival
- Competing risks models
- Frailty models
- Extension of frailty models
- Cure models

Multistate Models
- Introduction
- Multistate processes
- Multistate models: generalities
- Observation schemes
- Statistical inference for multistate models observed in continuous time
- Inference for multistate models from interval-censored data
- Complex functions of parameters: individualized hazards, sojourn times
- Approach by counting processes
- Other approaches

Joint Models for Longitudinal and Time-to-Event Data
- Introduction
- Models with shared random effects
- Latent class joint model
- Latent classes versus shared random effects
- The joint model as prognostic model
- Extension of joint models

The Dynamic Approach to Causality
- Introduction
- Local independence, direct and indirect influence
- Causal influences
- The dynamic approach to causal reasoning in ageing studies
- Mechanistic models
- The issue of dynamic treatment regimes

Appendix: Software
Index


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Biography

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.

Hélène 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.