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.

Katherine E. Irimata is a mathematical statistician at the National Center for Health Statistics* in the Division of Research and Methodology. Brittany N. Dugger is an assistant professor of pathology and laboratory medicine at the University of California, Davis, where she is the neuropathology core coleader at the Alzheimer's Disease Center. Jeffrey R. Wilson is an associate professor of statistics and biostatistics at Arizona State University. He is the coauthor of Modeling Binary Correlated Responses using SAS, SPSS and R and the coeditor of Innovative Statistical Methods for Public Health Data. *Katherine E. Irimata is serving in her personal capacity, and the work for this book was initiated and conducted when she was a student at Arizona State University. The views expressed are those of the authors and do not necessarily represent the views of the National Center for Health Statistics, the Centers for Disease Control and Prevention, or the US government.