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.
