  Analyzing longitudinal data in health studies is challenging due to sparse
and error-prone measurements, strong within-individual correlation, missing
data and various trajectory shapes. While mixed-effect models (MM) effectively
address these challenges, they remain parametric models and may incur
computational costs. In contrast, Functional Principal Component Analysis
(FPCA) is a non-parametric approach developed for regular and dense functional
data that flexibly describes temporal trajectories at a potentially lower
computational cost. This paper presents an empirical simulation study
evaluating the behaviour of FPCA with sparse and error-prone repeated measures
and its robustness under different missing data schemes in comparison with MM.
The results show that FPCA is well-suited in the presence of missing at random
data caused by dropout, except in scenarios involving most frequent and
systematic dropout. Like MM, FPCA fails under missing not at random mechanism.
The FPCA was applied to describe the trajectories of four cognitive functions
before clinical dementia and contrast them with those of matched controls in a
case-control study nested in a population-based aging cohort. The average
cognitive declines of future dementia cases showed a sudden divergence from
those of their matched controls with a sharp acceleration 5 to 2.5 years prior
to diagnosis.
