Additive quantile mixed effects modelling with application to longitudinal CD4 count data.

ABSTRACT: Quantile regression offers an invaluable tool to discern effects that would be missed by other conventional regression models, which are solely based on modeling conditional mean. Quantile regression for mixed-effects models has become practical for longitudinal data analysis due to the recent computational advances and the ready availability of efficient linear programming algorithms. Recently, quantile regression has also been extended to additive mixed-effects models, providing an efficient and flexible framework for nonparametric as well as parametric longitudinal forms of data analysis focused on features of the outcome beyond its central tendency. This study applies the additive quantile mixed model to analyze the longitudinal CD4 count of HIV-infected patients enrolled in a follow-u

SUBMITTER: Yirga AA 

PROVIDER: S-EPMC8429740 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature