Fundamental Statistical Methods for Analysis of Alzheimer's and Other Neurodegenerative Diseases

This Book offers a comprehensive guide to statistical methods for analyzing data, particularly in the context of Alzheimer's disease research. It explains fundamental statistical concepts like parameters, statistics, confidence intervals, and likelihood. The book provides hands-on demonstrations using popular statistical software packages such as SAS, R, SPSS, and Stata. Various statistical models are explored, including linear regression, logistic regression, generalized linear models, mixed models for hierarchical data, Bayesian methods, survival analysis for time-to-event data, and techniques for handling time-dependent covariates and joint modeling of mean and dispersion. Furthermore, it introduces machine learning techniques like neural networks and includes a case study to illustrate the practical application of these methods in analyzing real-world Alzheimer's disease data from sources like the National Alzheimer’s Coordinating Center (NACC).

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