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Activity Number: 609 - New Approaches to Improving Accuracy, Precision, and Robustness of Survival Analysis
Type: Contributed
Date/Time: Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #304662 Presentation
Title: A Machine Learning Approach to Multivariate Frailty Models
Author(s): Jing Wang*
Companies: The University of Texas at Arlington
Keywords: Multivariate frailty models ; Neural networks; Partial likelihood

Multivariate frailty models have been used for clustered survival data to characterize the relationship between the hazard of correlated failures/events and independent variables. These models assume that the logarithm of risk of failure is a linear function of the independent variables, which can be difficult to verify and often are not suitable for many complex clinical applications. We develop a machine learning algorithm to integrate the random-effects structure into the neural networks to model nonlinear multivariate frailty data without a priori assumptions. We demonstrate the effectiveness of the proposed method using simulations and real data sets.

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

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