Online Program Home
My Program

Abstract Details

Activity Number: 408 - Joint Modeling of Longitudinal and Survival Data and Related Topics
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: Lifetime Data Science Section
Abstract #304569
Title: The Joint Modeling of Longitudinal Covariates and Censored Quantile Regression
Author(s): Bo Hu* and Ying Wei and Mary Beth Terry
Companies: Columbia University and Columbia University, Biostatistics Department and Columbia University
Keywords: Censored Quantile Regression; Longitudinal Covariates; Joint Modelling

Censored quantile regressions can model a survival outcome without pre-specifying a parametric likelihood function or assuming a proportional hazard ratio. Existing censored quantile methods are mostly limited to fixed cross-sectional covariates, while in many longitudinal studies, researchers wish to investigate the associations between longitudinal covariates and a survival outcome. We propose a framework that jointly model a longitudinal covariate process, conditional quantiles of a survival outcome and their associations. This framework is an extension of a censored quantile based data augmentation algorithm (Yang, Narisetty and He, 2018), to allow for a longitudinal covariate process in presence of different forms of censoring schemes. We apply the proposed method to the LEGACY Girls cohort Study to understand the influence of individual genetic profiles on the pubertal development (i.e., the onset of breast development) while adjusting for BMI growth trajectories. The results are compared favorably to the likelihood based joint modeling of survival and longitudinal data. In the meantime, we found new insight on the puberty growth of girls with breast cancer family history.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program