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Activity Number: 454 - Advances and Applications of Joint Modeling for Longitudinal and Time-To-Event Data
Type: Invited
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: WNAR
Abstract #326838 Presentation
Title: Improve Risk Prediction Model Estimation with Longitudinal Surrogate Markers
Author(s): Yu Zheng* and Tianxi Cai and Lu Tian
Companies: Harvard T.H. Chan School of Public Health and Harvard T.H. Chan School of Public Health and Stanford University School of Medicine
Keywords: Risk prediction; survival; intermediate outcomes; efficiency augmentation; Model mis-spcification; Robustness

Risk prediction plays an important role in precision medicine. In many clinical settings, it is of great interest to develop models for predicting the t-year risk of developing a clinical event using baseline covariates. Such t-year risk models can be estimated by fitting a flexible time specific generalized linear model (GLM). However, efficient and robust estimation of the risk model is challenging under heavy censoring. Incorporating intermediate outcome information could potentially improve the efficiency of the prediction model. However, existing augmentation methods largely do not allow intermediate outcomes to be subject to censoring and may yield invalid results under model mis-specification. In this paper, we propose a two-step augmentation method to improve the estimation of t-year risk model by leveraging longitudinally collected intermediate outcome information that is subject to censoring. We also propose resampling methods to assess the variability of our proposed estimators. Numerical studies show that the proposed procedures perform well in finite sample. We also illustrate the proposed methods using data from Prevention Diabetes Program.

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

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