Modeling Temporal Biomarkers With Semiparametric Nonlinear Dynamical Systems.

ABSTRACT: Dynamical systems based on differential equations are useful for modeling the temporal evolution of biomarkers. These systems can characterize the temporal patterns of biomarkers and inform the detection of interactions among biomarkers. Existing statistical methods for dynamical systems mostly target single time-course data based on a linear model or generalized additive model. Hence, they cannot adequately capture the complex interactions among biomarkers; neither can they take into account the heterogeneity between systems or subjects. in this work, we propose a semiparametric dynamical system based on multi-index models for multiple subjects time-course data. Our model accounts for between-subject heterogeneity by introducing system-level or subject-level covariates to dynamic systems,

SUBMITTER: Sun BM 

PROVIDER: S-EPMC8315107 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature