Fundamental statistical methods for analysis of Alzheimer's and other neurodegenerative diseases / Katherine E. Irimata, Brittany N. Dugger, Jeffrey R. Wilson.

Includes bibliographical references and index.

Introduction to Statistical Software and Alzheimer's Data -- Review of Introductory Statistical Methods -- Generalized Linear Models -- Hierarchical Regression Models for Continuous Responses -- Hierarchical Logistic Regression Models -- Bayesian Regression Models -- Multiple Membership Models -- Survival Data Analysis -- Modeling Responses with Time-Dependent Covariates -- Joint Modeling of Mean and Dispersion -- Neural Networks and Other Machine Learning Techniques for Big Data -- Case Study.

"This book explains statistical techniques commonly used in analyzing data for Alzheimer's and other neurodegenerative diseases, and it presents examples from real-world applications in an effort to make the techniques useful for professionals and students. The book leads readers through the steps of conducting multivariate analyses while adjusting for correlation or the hierarchical structure of data in prediction and inferences. Techniques such as spatial analysis, Bayesian analysis, and time-dependent covariates are included. Several data sets from the National Alzheimer's Coordinating Center are analyzed with statistical software commonly used by Alzheimer's researchers, and the results are shown to readers by way of illustration"-- Provided by publisher

Online resource; title from digital title page (viewed on April 13, 2020).

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