Boosting Distributional Copula Regression for Bivariate Right-Censored Time-to-Event Data Article Swipe
Guillermo Briseno-Sanchez
,
Nadja Klein
,
Andreas Groll
,
Andreas Mayr
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.15041
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.15041
We propose a highly flexible distributional copula regression model for bivariate time-to-event data in the presence of right-censoring. The joint survival function of the response is constructed using parametric copulas, allowing for a separate specification of the dependence structure between the time-to-event outcome variables and their respective marginal survival distributions. The latter are specified using well-known parametric distributions such as the log-Normal, log-Logistic (proportional odds model), or Weibull (proportional hazards model) distributions. Hence, the marginal univariate event times can be specified as parametric (also known as Accelerated Failure Time, AFT) models. Embedding our model into the class of generalized additive models for location, scale and shape, possibly all distribution parameters of the joint survival function can depend on covariates. We develop a component-wise gradient-based boosting algorithm for estimation. This way, our approach is able to conduct data-driven variable selection. To the best of our knowledge, this is the first implementation of multivariate AFT models via distributional copula regression with automatic variable selection via statistical boosting. A special merit of our approach is that it works for high-dimensional (p>>n) settings. We illustrate the practical potential of our method on a high-dimensional application related to semi-competing risks responses in ovarian cancer. All of our methods are implemented in the open source statistical software R as add-on functions of the package gamboostLSS.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2412.15041
- https://arxiv.org/pdf/2412.15041
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4405645221Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2412.15041Digital Object Identifier
- Title
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Boosting Distributional Copula Regression for Bivariate Right-Censored Time-to-Event DataWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-12-19Full publication date if available
- Authors
-
Guillermo Briseno-Sanchez, Nadja Klein, Andreas Groll, Andreas MayrList of authors in order
- Landing page
-
https://arxiv.org/abs/2412.15041Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2412.15041Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2412.15041Direct OA link when available
- Concepts
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Copula (linguistics), Bivariate analysis, Boosting (machine learning), Econometrics, Statistics, Regression, Event data, Computer science, Mathematics, Artificial intelligence, CovariateTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.time-to-event | 11, 41 |
| abstract_inverted_index.component-wise | 122 |
| abstract_inverted_index.distributional | 5, 155 |
| abstract_inverted_index.distributions. | 49, 71 |
| abstract_inverted_index.gradient-based | 123 |
| abstract_inverted_index.implementation | 149 |
| abstract_inverted_index.semi-competing | 193 |
| abstract_inverted_index.high-dimensional | 176, 189 |
| abstract_inverted_index.right-censoring. | 17 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |