Statistical Approaches to Longitudinal Data Analysis in Neurodegenerative Diseases: Huntington’s Disease as a Model

• Published in: Current Neurology and Neuroscience Reports Vol. 17; no. 2; p. 14
• Main Authors: Garcia, Tanya P., Marder, Karen
• Format: Journal Article Book Review
• Language: English
• Published: New York Springer US 01.02.2017; Springer Nature B.V
• Subjects: Data Interpretation, Statistical; Dementia (KS Marder; Disease Progression; Humans; Huntington Disease - diagnosis; Longitudinal Studies; Medicine; Medicine & Public Health; Models, Statistical; Neurodegenerative Diseases - diagnosis; Neurology; Neurosciences; Section Editor; Topical Collection on Dementia; Mixed effect models; Missing data; Time-varying effects; Generalized estimating equations; Longitudinal study
• ISSN: 1528-4042; 1534-6293
• DOI: 10.1007/s11910-017-0723-4

Understanding the overall progression of neurodegenerative diseases is critical to the timing of therapeutic interventions and design of effective clinical trials. Disease progression can be assessed with longitudinal study designs in which outcomes are measured repeatedly over time and are assessed with respect to risk factors, either measured repeatedly or at baseline. Longitudinal data allows researchers to assess temporal disease aspects, but the analysis is complicated by complex correlation structures, irregularly spaced visits, missing data, and mixtures of time-varying and static covariate effects. We review modern statistical methods designed for these challenges. Among all methods, the mixed effect model most flexibly accommodates the challenges and is preferred by the FDA for observational and clinical studies. Examples from Huntington’s disease studies are used for clarification, but the methods apply to neurodegenerative diseases in general, particularly as the identification of prodromal forms of neurodegenerative disease through sensitive biomarkers is increasing.