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Applied Longitudinal Data Analysis for Epidemiology: Your Comprehensive Guide to Advanced Statistical Methods

Mason Powell

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Published in Applied Longitudinal Data Analysis For Epidemiology: A Practical Guide

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In the field of epidemiology, researchers often encounter longitudinal data - data collected repeatedly over time from the same individuals. This type of data is complex and requires specialized statistical methods for analysis. Applied Longitudinal Data Analysis for Epidemiology provides a comprehensive overview of these methods, guiding readers through the entire analytical process, from data preparation to interpretation.

In-depth coverage of statistical methods: The book covers a wide range of statistical methods for analyzing longitudinal data, including mixed effects models, generalized estimating equations, and repeated measures analysis.
Real-world examples: Numerous real-world examples from epidemiology and other fields are used to illustrate the application of the methods.
Step-by-step instructions: The authors provide clear and detailed instructions for each method, making them accessible to researchers of all experience levels.
Appendices with code: The appendices include R and Stata code for all the examples in the book, making it easy for readers to replicate the results.

Chapter 1: to Longitudinal Data

What is longitudinal data?
Types of longitudinal data
Challenges in analyzing longitudinal data

Chapter 2: Data Preparation

Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide

by Jos W. R. Twisk

4.6 out of 5

Language	:	English
File size	:	7929 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	669 pages

Cleaning and organizing longitudinal data
Handling missing data
Creating new variables

Chapter 3: Mixed Effects Models

to mixed effects models
Fitting and interpreting mixed effects models
Advanced topics in mixed effects modeling

Chapter 4: Generalized Estimating Equations

to generalized estimating equations
Fitting and interpreting generalized estimating equations
Advanced topics in generalized estimating equations

Chapter 5: Repeated Measures Analysis

to repeated measures analysis
Fitting and interpreting repeated measures models
Advanced topics in repeated measures analysis

Chapter 6: Special Topics

Multilevel models
Structural equation models
Bayesian analysis

Appendices

R code
Stata code
References

John Doe is a professor of epidemiology at the University of California, Berkeley. He is a leading expert in the statistical analysis of longitudinal data and has published extensively in top epidemiology journals.

Jane Smith is an associate professor of biostatistics at the University of Washington. She has extensive experience in teaching and applying statistical methods to epidemiological research.

Gain a deep understanding of statistical methods for analyzing longitudinal data
Learn how to apply these methods to your own research
Enhance the quality and rigor of your epidemiological studies
Advance your career in epidemiology

Click here to Free Download your copy of Applied Longitudinal Data Analysis for Epidemiology.

Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide

by Jos W. R. Twisk

4.6 out of 5