A Flexible Joint Longitudinal-Survival Model for Analyzing Longitudinally Sampled Biomarkers

作     者：Sepehr Akhavan Masouleh Tracy Holsclaw Babak Shahbaba Daniel L. Gillen Sepehr Akhavan Masouleh;Tracy Holsclaw;Babak Shahbaba;Daniel L. Gillen

作者机构：Facebook Menlo Park USA Department of Computer Science University of California Irvine USA Department of Statistics University of California Irvine USA 

出 版 物：《Open Journal of Statistics》 (统计学期刊（英文）)

年 卷 期：2021年第11卷第5期

页      面：778-805页

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

主　　题：Joint Longitudinal-Survival Bayesian Nonparameterics Gaussian Processes 

摘      要：We propose a flexible joint longitudinal-survival framework to examine the association between longitudinally collected biomarkers and a time-to-event endpoint. More specifically, we use our method for analyzing the survival outcome of end-stage renal disease patients with time-varying serum albumin measurements. Our proposed method is robust to common parametric assumptions in that it avoids explicit specification of the distribution of longitudinal responses and allows for a subject-specific baseline hazard in the survival component. Fully joint estimation is performed to account for uncertainty in the estimated longitudinal biomarkers that are included in the survival model.