Work

Structuring, sequencing, staging, selecting: the 4S method for the longitudinal analysis of multidimensional questionnaires in chronic diseases

Year: 2025

Type: article

Abstract: In clinical studies, questionnaires are often used to report disease-related manifestations from clinician and/or patient perspectives. Their analysis can help identify relevant manifestations throughout the disease course, enhancing knowledge of disease progression and guiding clinicians in appropriate care provision. However, the analysis of questionnaires in health studies is not straightforward as made of repeated, ordinal, and potentially multidimensional item data. Sum-score summaries may considerably reduce information and hamper interpretation; items' changes over time occur along clinical progression; and as many other longitudinal processes, observations may be truncated by events. This work establishes a comprehensive strategy in four consecutive steps to leverage repeated ordinal data from multidimensional questionnaires. The 4S method successively (1) identifies the questionnaire structure into dimensions satisfying three calibration assumptions (unidimensionality, conditional independence, increasing monotonicity), (2) describes each dimension progression using a joint latent process model which includes a continuous-time item response theory model for the longitudinal subpart, (3) aligns each dimension progression with disease stages through a projection approach, and (4) identifies the most informative items across disease stages using the Fisher information. The method is applied to multiple system atrophy (MSA), a rare neurodegenerative disease, with the analysis of daily activity and motor impairments over disease progression. The 4S method provides an effective and complete analytical strategy for questionnaires repeatedly collected in health studies.

Source: Biometrics

DOI: 10.1093/biomtc/ujaf163

Authors: Tiphaine Saulnier, Wassilios G Meissner, Margherita Fabbri, Alexandra Foubert-Samier, Cécile Proust-Lima