Multivariatetsemiparametric mixed-effects model for longitudinal data with multiple characteristics

Semiparametric mixed-effects models (SMM) have received increasing attention in recent years because of the greater flexibility in analysing longitudinal trajectories. However, the normality assumption of SMM may be unrealistic when outliers occur in the data. This paper presents a semiparametric extension of the multivariatetlinear mixed-effects model (MtLMM), called the multivariatetsemiparametric mixed model (MtSMM). To be specific, the MtSMM incorporates a parametric linear function related to the fixed covariate effects and random effects which have a joint multivariatetdistribution together with an arbitrary nonparametric smooth function to capture the unexpected patterns. A computationally analytical EM-based algorithm is developed for carrying out maximum likelihood estimation of the MtSMM. Simulation studies and a real example concerning the analysis of PBCseq data are used to investigate the empirical behaviour of the proposed methodology.

Automated summary

This paper introduces a new multivariate semiparametric mixed model (MtSMM) which combines a parametric linear function, random effects, and a nonparametric smooth function to improve robustness against outliers, providing greater flexibility in analyzing longitudinal trajectories. Simulation studies and a real example on PBCseq data demonstrate the empirical behavior of the proposed methodology.