Dynamical biostatistical models Commenges, Daniel

By:

Commenges, Daniel

Contributor(s):

Jacqmin-Gadda, Helene

Series: Chapman and Hall/CRC Biostatistics seriesPublication details: CRC Press 2016 Boca RatonDescription: xxxiv, 374 pISBN:

9781498729673

Subject(s):

DDC classification:

614.4 C6D9

Summary: 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. The book 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. (https://www.crcpress.com/Dynamical-Biostatistical-Models/Commenges-JacqminGadda/9781498729673)

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	Ahmedabad	Non-fiction	614.4 C6D9 (Browse shelf(Opens below))	Available		190982

Total holds: 0

Table of 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

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.
The book 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.

(https://www.crcpress.com/Dynamical-Biostatistical-Models/Commenges-JacqminGadda/9781498729673)

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