While the examples are in the context of HD, methods presented are relevant to neurodegenerative diseases in general, particularly as the identification of prodromal forms of neurodegenerative disease through sensitive biomarkers is increasing. Compared to Alzheimer’s and Parkinson’s diseases, HD is less complicated, in that the genetic cause of HD absolutely predicts whether or not the person will develop HD and the CAG repeat length is correlated with age at onset. Throughout, methods are described using examples from HD, a progressive, primarily single-gene disorder with complete penetrance that can be genetically diagnosed years before clinical symptoms onset. As such, it is recommended by the FDA in analysis of observational studies and clinical trials. Among all methods discussed, the mixed effect regression model (“Mixed effects regression (MER)” Section) is most flexible and designed to handle multiples challenges of longitudinal data. In this paper, we review statistical techniques for analyzing longitudinal data for neurodegenerative diseases. Confusion on these points can lead to inappropriate and inaccurate analysis. Modern statistical methods handle these complications, but hindrances are knowing when to use these methods, verifying their assumptions, and interpreting their output correctly. These include data that are missing, correlated, and collected at irregularly spaced visits.
Īnalyzing longitudinal data is complicated, however, by practical and theoretical issues. Moreover, compared to cross-sectional studies, longitudinal studies often have less variability and increased statistical power.
Assessing longitudinal temporal changes is central to learning specific time patterns of clinical impairments that could be missed otherwise. Longitudinal data allow researchers to assess multiple disease aspects: changes of outcome(s) over time in relation to associated risk factors, timing of disease onset, and individual and group patterns over time. Longitudinal assessments of motor and cognitive impairments have also revealed insights into the natural progression of HD. In Huntington’s disease (HD), for example, longitudinal studies have assessed the effect of medication use on performance of motor, cognitive, and neuropsychiatric function over time. Disease progression can be assessed via longitudinal studies that measure outcomes repeatedly over time in relation to risk factors. Understanding the overall progression of neurodegenerative diseases is critical to the timing of therapeutic interventions and design of effective clinical trials. 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. Among all methods, the mixed effect model most flexibly accommodates the challenges and is preferred by the FDA for observational and clinical studies. We review modern statistical methods designed for these challenges. 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. 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.