Survival Trees for Current Status Data

Ce Yang, Liqun Diao, Richard Cook
Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, PMLR 146:83-94, 2021.

Abstract

Current status data arise when the exact time of an event of interest is not known and the only available information about the time is whether the time is beyond a single assessment. When interest lies in prediction based on such data, we define observed data loss functions through censoring unbiased transformations and pseudo-observations to construct unbiased estimates of complete data loss functions, and we use these to fit regression trees and make predictions using current status data. The trees grown based on these methods are found have good properties empirically in terms of recovery of the true tree structure and event time prediction.

Cite this Paper

BibTeX
@InProceedings{pmlr-v146-yang21a, title = {Survival Trees for Current Status Data}, author = {Yang, Ce and Diao, Liqun and Cook, Richard}, booktitle = {Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021}, pages = {83--94}, year = {2021}, editor = {Greiner, Russell and Kumar, Neeraj and Gerds, Thomas Alexander and van der Schaar, Mihaela}, volume = {146}, series = {Proceedings of Machine Learning Research}, month = {22--24 Mar}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v146/yang21a/yang21a.pdf}, url = {https://proceedings.mlr.press/v146/yang21a.html}, abstract = {Current status data arise when the exact time of an event of interest is not known and the only available information about the time is whether the time is beyond a single assessment. When interest lies in prediction based on such data, we define observed data loss functions through censoring unbiased transformations and pseudo-observations to construct unbiased estimates of complete data loss functions, and we use these to fit regression trees and make predictions using current status data. The trees grown based on these methods are found have good properties empirically in terms of recovery of the true tree structure and event time prediction.} }
Endnote
%0 Conference Paper %T Survival Trees for Current Status Data %A Ce Yang %A Liqun Diao %A Richard Cook %B Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021 %C Proceedings of Machine Learning Research %D 2021 %E Russell Greiner %E Neeraj Kumar %E Thomas Alexander Gerds %E Mihaela van der Schaar %F pmlr-v146-yang21a %I PMLR %P 83--94 %U https://proceedings.mlr.press/v146/yang21a.html %V 146 %X Current status data arise when the exact time of an event of interest is not known and the only available information about the time is whether the time is beyond a single assessment. When interest lies in prediction based on such data, we define observed data loss functions through censoring unbiased transformations and pseudo-observations to construct unbiased estimates of complete data loss functions, and we use these to fit regression trees and make predictions using current status data. The trees grown based on these methods are found have good properties empirically in terms of recovery of the true tree structure and event time prediction.
APA
Yang, C., Diao, L. & Cook, R.. (2021). Survival Trees for Current Status Data. Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, in Proceedings of Machine Learning Research 146:83-94 Available from https://proceedings.mlr.press/v146/yang21a.html.

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