title: Statistical Approaches to Longitudinal Data Analysis in Neurodegenerative Diseases: Huntington's Disease as a Model.
abstract: 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.
authors.affiliation: Department of Epidemiology and Biostatistics, Texas A&M University, TAMU 1266, College Station, TX, 77843-1266, USA. tpgarcia@sph.tamhsc.edu.
authors.forename: Tanya P
authors.initials: TP
authors.lastname: Garcia
authors.affiliation: Department of Neurology, Columbia University, 630 West 168th Street, New York, NY, 10032, USA.
authors.forename: Karen
authors.initials: K
authors.lastname: Marder
mesh_terms: D003627:Data Interpretation, Statistical; D018450:Disease Progression; D006801:Humans; D006816:Huntington Disease / Q000175:diagnosis*; D008137:Longitudinal Studies; D015233:Models, Statistical; D019636:Neurodegenerative Diseases / Q000175:diagnosis*
keywords: Generalized estimating equations; Longitudinal study; Missing data; Mixed effect models; Time-varying effects