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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 geneexpression data, we illustrate the unique insights into the overall discriminatory capability provided by the VUS.
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Prognostic accuracy for predicting ordinal competing risk outcomes using ROC surfaces
Many medical conditions are marked by a sequence of events in association with continuous changes in biomarkers. Few works have evaluated the overall accuracy of a biomarker in predicting disease progression. We thus extend the concept of receiver operating characteristic (ROC) surface and the volume under the surface (VUS) from multi-category outcomes to ordinal competing-risk outcomes that are also subject to noninformative censoring. Two VUS estimators are considered. One is based on the definition of the ROC surface and obtained by integrating the estimated ROC surface. The other is an inverse probability weighted U estimator that is built upon the equivalence of the VUS to the concordance probability between the marker and sequential outcomes. Both estimators have nice asymptotic results that can be derived using counting process techniques and U-statistics theory.We illustrate their good practical performances through simulations and applications to two studies of cognition and a transplant dataset.
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- Award ID(s):
- 1916001
- PAR ID:
- 10556492
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- Lifetime Data Analysis
- Volume:
- 28
- Issue:
- 1
- ISSN:
- 1380-7870
- Page Range / eLocation ID:
- 1 to 22
- 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/10.1007/s10985-021-09539-z
