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

A Flexible Joint Longitudinal-Survival Model for Analyzing Longitudinally Sampled Biomarkers
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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. 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.
作者 Sepehr Akhavan Masouleh Tracy Holsclaw Babak Shahbaba Daniel L. Gillen Sepehr Akhavan Masouleh;Tracy Holsclaw;Babak Shahbaba;Daniel L. Gillen(Facebook, Menlo Park, USA;Department of Computer Science, University of California, Irvine, USA;Department of Statistics, University of California, Irvine, USA)
出处 《Open Journal of Statistics》 2021年第5期778-805,共28页 统计学期刊(英文)
关键词 Joint Longitudinal-Survival Bayesian Nonparameterics Gaussian Processes Joint Longitudinal-Survival Bayesian Nonparameterics Gaussian Processes
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