Description
Book SynopsisA 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 Natio
Table of Contents1. 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
