Comparison of Longitudinal Trajectories Using a High-dimensional Partial Linear Semiparametric Mixed-Effects Model.

ABSTRACT: In longitudinal research, it is essential to compare sets of trajectories, commonly seen as changes over time in different treatment or patient groups. This paper presents a partial linear semiparametric mixed-effects model (PLSMM) for the analysis and comparison of nonlinear longitudinal trajectories with high-dimensional covariates across groups. Our flexible modeling framework can effectively handle complex temporal effects and extensive data while providing statistical inference. This method is particularly useful for evaluating differences in both linear and nonlinear components between groups, with a key strength being its ability to model nonlinear patterns without requiring prior knowledge of the functional forms. Instead, it employs a dictionary search strategy to automatically se

SUBMITTER: Leon S 

PROVIDER: S-EPMC12530507 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature