Temporal Quilting for Survival Analysis

Changhee Lee, William Zame, Ahmed Alaa, Mihaela Schaar
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:596-605, 2019.

Abstract

The importance of survival analysis in many disciplines (especially in medicine) has led to the development of a variety of approaches to modeling the survival function. Models constructed via various approaches offer different strengths and weaknesses in terms of discriminative performance and calibration, but no one model is best across all datasets or even across all time horizons within a single dataset. Because we require both good calibration and good discriminative performance over different time horizons, conventional model selection and ensemble approaches are not applicable. This paper develops a novel approach that combines the collective intelligence of different underlying survival models to produce a valid survival function that is well-calibrated and offers superior discriminative performance at different time horizons. Empirical results show that our approach provides significant gains over the benchmarks on a variety of real-world datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v89-lee19a, title = {Temporal Quilting for Survival Analysis}, author = {Lee, Changhee and Zame, William and Alaa, Ahmed and van der Schaar, Mihaela}, booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics}, pages = {596--605}, year = {2019}, editor = {Chaudhuri, Kamalika and Sugiyama, Masashi}, volume = {89}, series = {Proceedings of Machine Learning Research}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v89/lee19a/lee19a.pdf}, url = {https://proceedings.mlr.press/v89/lee19a.html}, abstract = {The importance of survival analysis in many disciplines (especially in medicine) has led to the development of a variety of approaches to modeling the survival function. Models constructed via various approaches offer different strengths and weaknesses in terms of discriminative performance and calibration, but no one model is best across all datasets or even across all time horizons within a single dataset. Because we require both good calibration and good discriminative performance over different time horizons, conventional model selection and ensemble approaches are not applicable. This paper develops a novel approach that combines the collective intelligence of different underlying survival models to produce a valid survival function that is well-calibrated and offers superior discriminative performance at different time horizons. Empirical results show that our approach provides significant gains over the benchmarks on a variety of real-world datasets.} }
Endnote
%0 Conference Paper %T Temporal Quilting for Survival Analysis %A Changhee Lee %A William Zame %A Ahmed Alaa %A Mihaela Schaar %B Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Masashi Sugiyama %F pmlr-v89-lee19a %I PMLR %P 596--605 %U https://proceedings.mlr.press/v89/lee19a.html %V 89 %X The importance of survival analysis in many disciplines (especially in medicine) has led to the development of a variety of approaches to modeling the survival function. Models constructed via various approaches offer different strengths and weaknesses in terms of discriminative performance and calibration, but no one model is best across all datasets or even across all time horizons within a single dataset. Because we require both good calibration and good discriminative performance over different time horizons, conventional model selection and ensemble approaches are not applicable. This paper develops a novel approach that combines the collective intelligence of different underlying survival models to produce a valid survival function that is well-calibrated and offers superior discriminative performance at different time horizons. Empirical results show that our approach provides significant gains over the benchmarks on a variety of real-world datasets.
APA
Lee, C., Zame, W., Alaa, A. & Schaar, M.. (2019). Temporal Quilting for Survival Analysis. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 89:596-605 Available from https://proceedings.mlr.press/v89/lee19a.html.

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