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Activity Number:
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83
- Recent Methodological Developments and Applications in Statistical and Machine Learning Approaches for Predictive Modeling Using Competing Risk Data
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Type:
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Topic-Contributed
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Date/Time:
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Monday, August 9, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Section on Risk Analysis
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Abstract #317120
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Title:
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Scalable Algorithms for Large-Scale Competing Risks Data
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Author(s):
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Eric Kawaguchi* and Jenny Shen and Gang Li and Marc Suchard
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Companies:
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University of Southern California and University of California Los Angeles and University of California Los Angeles and University of California Los Angeles
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Keywords:
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Fine-Gray model;
inverse-censoring probability;
large-scale data;
scalable computing;
semi-parametric modeling;
survival analysis
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Abstract:
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Advancements in medical informatics tools and high-throughput biological experimentation make large-scale biomedical data routinely accessible to researchers. Competing risks data are typical in biomedical studies where individuals are at risk to more than one cause (type of event) which can preclude the others from happening. The Fine-Gray model is a popular and well-appreciated model for competing risks data and is currently implemented in a number of statistical software packages. However, current implementations are not computationally scalable for large-scale competing risks data. We have developed an R package, fastcmprsk, that uses a novel forward-backward scan algorithm to significantly reduce the computational complexity for parameter estimation by exploiting the structure of the subject-specific risk sets. Numerical studies compare the speed and scalability of our implementation to current methods for unpenalized and penalized Fine-Gray regression and show impressive gains in computational efficiency.
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Authors who are presenting talks have a * after their name.
