While we emphasize that sophisticated and advanced analytic models cannot, and should not, compensate for poor study design and execution, we also maintain that solely using simplistic analytic methods can scuttle detection of important signals and effects, even in well-designed and -conducted studies. This lack of understanding can lead to inappropriate or inefficient analysis, inaccurate results, and simplistic or wrong interpretations, conclusions, and judgments. We feel there is too often a general lack of understanding and confusion concerning appropriate longitudinal data analysis methods that has bred insecurity towards or prejudice against the use of newer, advanced and more powerful methods among some clinical researchers and journal reviewers of neurological literature. Substantively, the special focus is on neurological conditions, especially dementia, but the methods are more broadly relevant. This paper provides a broad didactic survey of methods for statistical analysis of longitudinal, clinical, observational and experimental data, illustrated by applied examples, aimed to be of practical utility for clinical researchers with little background in statistical modeling.
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