Quantile regression analysis of censored longitudinal data with irregular outcome-dependent follow-up.

ABSTRACT: In many observational longitudinal studies, the outcome of interest presents a skewed distribution, is subject to censoring due to detection limit or other reasons, and is observed at irregular times that may follow a outcome-dependent pattern. In this work, we consider quantile regression modeling of such longitudinal data, because quantile regression is generally robust in handling skewed and censored outcomes and is flexible to accommodate dynamic covariate-outcome relationships. Specifically, we study a longitudinal quantile regression model that specifies covariate effects on the marginal quantiles of the longitudinal outcome. Such a model is easy to interpret and can accommodate dynamic outcome profile changes over time. We propose estimation and inference procedures that can appropr

SUBMITTER: Sun X 

PROVIDER: S-EPMC4740290 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature