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Activity Number:
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53
- New Developments in Survival Analysis
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Type:
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Contributed
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Date/Time:
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Sunday, August 8, 2021 : 3:30 PM to 5:20 PM
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Sponsor:
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Biometrics Section
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Abstract #318501
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Title:
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Penalized Weighted Proportional Hazards Model for Robust Survival Data Analysis
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Author(s):
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Bin Luo* and Xiaoli Gao and Susan Halabi
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Companies:
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Duke University and The University of North Carolina at Greensboro and Duke University
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Keywords:
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Outlier detection;
Robust regression;
High-dimensional data;
Proportional hazards model;
Censored data
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Abstract:
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Identifying extreme responders or non-responders is a hot area of research in precision medicine. We investigate the outlier detection and robust regression problem in the proportional hazards model for censored survival outcomes. The main idea is to model the irregularity of each observation by assigning an individual weight to the hazard function. By applying a LASSO-type penalty on the log-transformation of the weight vector, the proposed method is able to perform outlier detection and robust regression simultaneously. The optimization problem can be transformed to a typical penalized maximum partial likelihood problem and thus it is easy to implement. We further extend our method to high-dimensional data, if sparsity exists in both the coe?cient vector and the weight vector. The performance of the proposed estimator is demonstrated with extensive simulation studies and applications to a real data example.
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Authors who are presenting talks have a * after their name.
