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