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The Fine‐Gray proportional sub‐distribution hazards (PSH) model is among the most popular regression model for competing risks time‐to‐event data. This article develops a fast safe feature elimination method, named PSH‐SAFE, for fitting the penalized Fine‐Gray PSH model with a Lasso (or adaptive Lasso) penalty. Our PSH‐SAFE procedure is straightforward to implement, fast, and scales well to ultrahigh dimensional data. We also show that as a feature screening procedure, PSH‐SAFE is safe in a sense that the eliminated features are guaranteed to be inactive features in the original Lasso (or adaptive Lasso) estimator for the penalized PSH model. We evaluate the performance of the PSH‐SAFE procedure in terms of computational efficiency, screening efficiency and safety, run‐time, and prediction accuracy on multiple simulated datasets and a real bladder cancer data. Our empirical results show that the PSH‐SAFE procedure possesses desirable screening efficiency and safety properties and can offer substantially improved computational efficiency as well as similar or better prediction performance in comparison to their baseline competitors.
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Cooperative learning for multiview analysis
We propose a method for supervised learning with multiple sets of features (“views”). The multiview problem is especially important in biology and medicine, where “-omics” data, such as genomics, proteomics, and radiomics, are measured on a common set of samples. “Cooperative learning” combines the usual squared-error loss of predictions with an “agreement” penalty to encourage the predictions from different data views to agree. By varying the weight of the agreement penalty, we get a continuum of solutions that include the well-known early and late fusion approaches. Cooperative learning chooses the degree of agreement (or fusion) in an adaptive manner, using a validation set or cross-validation to estimate test set prediction error. One version of our fitting procedure is modular, where one can choose different fitting mechanisms (e.g., lasso, random forests, boosting, or neural networks) appropriate for different data views. In the setting of cooperative regularized linear regression, the method combines the lasso penalty with the agreement penalty, yielding feature sparsity. The method can be especially powerful when the different data views share some underlying relationship in their signals that can be exploited to boost the signals. We show that cooperative learning achieves higher predictive accuracy on simulated data and real multiomics examples of labor-onset prediction. By leveraging aligned signals and allowing flexible fitting mechanisms for different modalities, cooperative learning offers a powerful approach to multiomics data fusion.
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- Award ID(s):
- 2113389
- PAR ID:
- 10355842
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 119
- Issue:
- 38
- ISSN:
- 0027-8424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1073/pnas.2202113119
