Full Description
A statistics textbook that delivers essential data analysis techniques for Alzheimer's and other neurodegenerative diseases.
Alzheimer's disease is a devastating condition that presents overwhelming challenges to patients and caregivers. In the face of this relentless and as-yet incurable disease, mastery of statistical analysis is paramount for anyone who must assess complex data that could improve treatment options. This unique book presents up-to-date statistical techniques commonly used in the analysis of data on Alzheimer's and other neurodegenerative diseases.
With examples drawn from the real world that will make it accessible to disease researchers, practitioners, academics, and students alike, this volume
• presents code for analyzing dementia data in statistical programs, including SAS, R, SPSS, and Stata
• introduces statistical models for a range of data types, including continuous, categorical, and binary responses, as well as correlated data
• draws on datasets from the National Alzheimer's Coordinating Center, a large relational database of standardized clinical and neuropathological research data
• discusses advanced statistical methods, including hierarchical models, survival analysis, and multiple-membership
• examines big data analytics and machine learning methods
Easy to understand but sophisticated in its approach, Fundamental Statistical Methods for Analysis of Alzheimer's and Other Neurodegenerative Diseases will be a cornerstone for anyone looking for simplicity in understanding basic and advanced statistical data analysis topics. Allowing more people to aid in analyzing data—while promoting constructive dialogues with statisticians—this book will hopefully play an important part in unlocking the secrets of these confounding diseases.
Contents
1. Introduction to Statistical Software and Alzheimer's Data
2. Review of Introductory Statistical Methods
3. Generalized Linear Models
4. Hierarchical Regression Models for Continuous Responses
5. Hierarchical Logistic Regression Models
6. Bayesian Regression Models
7. Multiple Membership Models
8. Survival Data Analysis
9. Modeling Responses with Time-dependent Covariates
10. Joint Modeling of Mean and Dispersion
11. Neural Networks and Other Machine Learning Techniques for Big Data
12. Case Study
References
Acknowledgments
