Bayesian quantile regression joint models: Inference and dynamic predictions.

ABSTRACT: In the traditional joint models of a longitudinal and time-to-event outcome, a linear mixed model assuming normal random errors is used to model the longitudinal process. However, in many circumstances, the normality assumption is violated and the linear mixed model is not an appropriate sub-model in the joint models. In addition, as the linear mixed model models the conditional mean of the longitudinal outcome, it is not appropriate if clinical interest lies in making inference or prediction on median, lower, or upper ends of the longitudinal process. To this end, quantile regression provides a flexible, distribution-free way to study covariate effects at different quantiles of the longitudinal outcome and it is robust not only to deviation from normality, but also to outlying observation

SUBMITTER: Yang M 

PROVIDER: S-EPMC6050160 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature