ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2019
Flexible and Interpretable Models for Survival Data.
Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Article
- Interpretable Machine Learning for Survival Analysis.Biometrical journal. Biometrische Zeitschrift · 2025Review
- Spike-and-slab least absolute shrinkage and selection operator generalized additive models and scalable algorithms for high-dimensional data analysis.Statistics in medicine · 2022Article
- Survival Mixture Density Networks.Proceedings of machine learning research · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
As datasets continue to increase in size, there is growing interest in methods for prediction that are both Received January 2018 flexible and interpretable. A flurry of recent work on this topic has focused on additive modeling in the Revised February 2019 regression setting, and in particular, on the use of data-adaptive nonlinear functions that can be used to flexibly model each covariate's effect, conditional on the other features in the model. In this article, we extend this recent line of work to the survival setting. We develop an additive Cox proportional hazards model, in which each additive function is obtained by trend filtering, so that the fitted functions are piece-wise polynomial with adaptively chosen knots. An efficient proximal gradient descent algorithm is used to fit the model. We demonstrate its performance in simulations and in application to a primary biliary cirrhosis data set, as well as a dataset consisting of time to publication for clinical trials in the biomedical literature. Supplementary materials for this article are available online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.