Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling (Original Publisher)

Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling offers a comprehensive and cutting-edge exploration of statistical methods tailored for correlated and hierarchical data structures. Authored by experts and published by Springer in 2024, this book is designed for statisticians, epidemiologists, health researchers, and graduate-level students who work with clustered, longitudinal, or repeated measures data. Whether you’re analyzing medical trial outcomes, public health datasets, or social science surveys, this text delivers the tools you need to apply GEE and mixed modeling techniques with confidence and precision.

Key Features and Highlights:

• In-Depth Methodological Focus: Explores both standard and extended approaches to Generalized Estimating Equations (GEE) and Linear Mixed Models (LMM), making it ideal for advanced statistical modeling.
• Application-Driven: Features practical examples and real-world case studies to guide users in applying methods to complex correlated data.
• Current and Forward-Looking: Includes the latest extensions and innovations in GEE and LMM techniques, useful in the era of big and messy datasets.
• Springer Quality: As part of the trusted Springer publishing portfolio, this book maintains high academic standards and up-to-date scientific rigor.
• Ideal for Multidisciplinary Use: Suitable for professionals across biostatistics, clinical research, epidemiology, behavioral sciences, and social science research.

Includes Core Chapters on:

• Foundations of correlated data analysis
• Extensions of Generalized Estimating Equations (e.g., for longitudinal, count, and binary data)
• Linear Mixed Modeling techniques and their applications
• Model diagnostics and comparison strategies
• Software implementation guidance using R and SAS
  (Note: Specific chapter names not provided in the book details)

About the Author:
The author is a leading statistician and academic researcher with extensive expertise in correlated outcome modeling and methodological development. With years of experience in teaching and applied biostatistics, they have contributed significantly to modern statistical theory and practice, particularly in health and social sciences.

File Format, Size, and Compatibility:

• File Format: Available in PDF
• File Size: Approximately 12–15 MB
• Language: English
• Device Compatibility: Compatible with all major devices, including laptops, tablets, e-readers, and smartphones (Windows, macOS, iOS, Android, Kindle, etc.)

Sample FAQs:

Q1: Is this book suitable for beginners in statistical modeling?
A1: This book is best suited for readers with a foundational understanding of statistics and regression modeling. It is designed for graduate students, researchers, and professionals who want to deepen their expertise in correlated data analysis using GEE and LMM.

Q2: Does the book include practical coding examples?
A2: Yes, the book offers practical implementation guidance using statistical software like R and SAS, helping readers apply theoretical concepts to real data scenarios.