  In clinical studies, measurement scales are often collected to report
disease-related manifestations from clinician or patient perspectives. Their
analysis can help identify relevant manifestations throughout the disease
course, enhancing knowledge of disease progression and guiding clinicians in
providing appropriate support. However, the analysis of measurement scales in
health studies is not straightforward as made of repeated, ordinal, and
potentially multidimensional item data. Their sum-score summaries may
considerably reduce information and impend interpretation, their change over
time occurs along clinical progression, and as many other longitudinal
processes, their observation may be truncated by events. This work establishes
a comprehensive strategy in four consecutive steps to leverage repeated data
from multidimensional measurement scales. The 4S method successively (1)
identifies the scale structure into subdimensions satisfying three calibration
assumptions (unidimensionality, conditional independence, increasing
monotonicity), (2) describes each subdimension progression using a joint latent
process model which includes a continuous-time item response theory model for
the longitudinal subpart, (3) aligns each subdimension's progression with
disease stages through a projection approach, and (4) identifies the most
informative items across disease stages using the Fisher's information. The
method is comprehensively illustrated in multiple system atrophy (MSA), an
alpha-synucleinopathy, with the analysis of daily activity and motor
impairments over disease progression. The 4S method provides an effective and
complete analytical strategy for any measurement scale repeatedly collected in
health studies.
