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Published in: Lifetime Data Analysis 4/2023

09-05-2023

Volume under the ROC surface for high-dimensional independent screening with ordinal competing risk outcomes

Authors: Yang Qu, Yu Cheng

Published in: Lifetime Data Analysis | Issue 4/2023

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Abstract

We propose a screening method for high-dimensional data with ordinal competing risk outcomes, which is time-dependent and model-free. Existing methods are designed for cause-specific variable screening and fail to evaluate how a biomarker is associated with multiple competing events simultaneously. The proposed method utilizes the Volume under the ROC surface (VUS), which measures the concordance between values of a biomarker and event status at certain time points and provides an overall evaluation of the discrimination capacity of a biomarker. We show that the VUS possesses the sure screening property, i.e., true important covariates can be retained with probability tending to one, and the size of the selected set can be bounded with high probability. The VUS appears to be a viable model-free screening metric as compared to some existing methods in simulation studies, and it is especially robust to data contamination. Through an analysis of breast-cancer gene-expression data, we illustrate the unique insights into the overall discriminatory capability provided by the VUS.

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Appendix
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Metadata
Title
Volume under the ROC surface for high-dimensional independent screening with ordinal competing risk outcomes
Authors
Yang Qu
Yu Cheng
Publication date
09-05-2023
Publisher
Springer US
Published in
Lifetime Data Analysis / Issue 4/2023
Print ISSN: 1380-7870
Electronic ISSN: 1572-9249
DOI
https://doi.org/10.1007/s10985-023-09600-z

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