A0717
Title: Dynamic regression of longitudinal trajectory features
Authors: Huijuan Ma - East China Normal University (China) [presenting]
Abstract: Chronic disease studies often collect data on biological and clinical markers at follow-up visits to monitor disease progression. Viewing such longitudinal measurements governed by latent continuous trajectories, a new dynamic regression framework is developed to investigate the heterogeneity pattern of certain features of the latent individual trajectory that may carry substantive information on disease risk or status. Employing the strategy of multi-level modeling, the latent individual trajectory feature of interest is formulated through a flexible pseudo B-spline model with subject-specific random parameters and then link it with the observed covariates through quantile regression, avoiding restrictive parametric distributional assumptions that are typically required by standard multi-level longitudinal models. An estimation procedure is proposed by adapting the principle of the conditional score and developing an efficient algorithm for implementation. The proposals yield estimators with desirable asymptotic properties as well as good finite-sample performance, as confirmed by extensive simulation studies. An application of the proposed method to a cohort of participants with mild cognitive impairment (MCI) in the Uniform Data Set (UDS) provides useful insights about the complex heterogeneous presentations of cognitive decline in MCI patients.
