Describing variability of intensively collected longitudinal ordinal data with latent spline models.

ABSTRACT: Population health studies increasingly collect longitudinal, patient-reported symptom data via mobile devices, offering unique insights into experiences outside clinical settings, such as pain, fatigue or mood. However, such data present challenges due to ordinal measurement scales, irregular sampling and temporal autocorrelation. This paper introduces two novel summary measures for analysing ordinal outcomes: (1) the mean absolute deviation from the median (MADM) for cross-sectional analyses and (2) the mean absolute deviation from expectation (MADE) for longitudinal data. The latter is based on a latent cumulative model with penalized splines, enabling smooth transitions between irregular time points while accounting for the ordinal nature of the data. Unlike black-box machine learning a

SUBMITTER: Lunt M 

PROVIDER: S-EPMC12488869 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature