Correlation-based joint feature screening for semi-competing risks outcomes with application to breast cancer data
Ultrahigh-dimensional gene features are often collected in modern cancer studies in which the number of gene features p is extremely larger than sample size n. While gene expression patterns have been shown to be related to patients' survival in microarray-based gene expression studies, one has...
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Online Access: | https://hdl.handle.net/10356/157027 |
| _version_ | 1811680661184970752 |
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| author | Peng, Mengjiao Xiang, Liming |
| author2 | School of Physical and Mathematical Sciences |
| author_facet | School of Physical and Mathematical Sciences Peng, Mengjiao Xiang, Liming |
| author_sort | Peng, Mengjiao |
| collection | NTU |
| description | Ultrahigh-dimensional gene features are often collected in modern cancer studies in which the number of gene features p is extremely larger than sample size n. While gene expression patterns have been shown to be related to patients' survival in microarray-based gene expression studies, one has to deal with the challenges of ultrahigh-dimensional genetic predictors for survival predicting and genetic understanding of the disease in precision medicine. The problem becomes more complicated when two types of survival endpoints, distant metastasis-free survival and overall survival, are of interest in the study and outcome data can be subject to semi-competing risks due to the fact that distant metastasis-free survival is possibly censored by overall survival but not vice versa. Our focus in this paper is to extract important features, which have great impacts on both distant metastasis-free survival and overall survival jointly, from massive gene expression data in the semi-competing risks setting. We propose a model-free screening method based on the ranking of the correlation between gene features and the joint survival function of two endpoints. The method accounts for the relationship between two endpoints in a simply defined utility measure that is easy to understand and calculate. We show its favorable theoretical properties such as the sure screening and ranking consistency, and evaluate its finite sample performance through extensive simulation studies. Finally, an application to classifying breast cancer data clearly demonstrates the utility of the proposed method in practice. |
| first_indexed | 2024-10-01T03:28:36Z |
| format | Journal Article |
| id | ntu-10356/157027 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:28:36Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1570272023-02-28T20:06:29Z Correlation-based joint feature screening for semi-competing risks outcomes with application to breast cancer data Peng, Mengjiao Xiang, Liming School of Physical and Mathematical Sciences Science::Mathematics Gene Expression Data Joint Survival Function Ultrahigh-dimensional gene features are often collected in modern cancer studies in which the number of gene features p is extremely larger than sample size n. While gene expression patterns have been shown to be related to patients' survival in microarray-based gene expression studies, one has to deal with the challenges of ultrahigh-dimensional genetic predictors for survival predicting and genetic understanding of the disease in precision medicine. The problem becomes more complicated when two types of survival endpoints, distant metastasis-free survival and overall survival, are of interest in the study and outcome data can be subject to semi-competing risks due to the fact that distant metastasis-free survival is possibly censored by overall survival but not vice versa. Our focus in this paper is to extract important features, which have great impacts on both distant metastasis-free survival and overall survival jointly, from massive gene expression data in the semi-competing risks setting. We propose a model-free screening method based on the ranking of the correlation between gene features and the joint survival function of two endpoints. The method accounts for the relationship between two endpoints in a simply defined utility measure that is easy to understand and calculate. We show its favorable theoretical properties such as the sure screening and ranking consistency, and evaluate its finite sample performance through extensive simulation studies. Finally, an application to classifying breast cancer data clearly demonstrates the utility of the proposed method in practice. Ministry of Education (MOE) Submitted/Accepted version Xiang’s research was supported by the Singapore Ministry of Education Academic Research Fund Tier 1 grant RG98/20 and Peng’s research was supported by National Natural Science Foundation of China (NSFC Grant No. 92046005). 2022-04-30T07:43:21Z 2022-04-30T07:43:21Z 2021 Journal Article Peng, M. & Xiang, L. (2021). Correlation-based joint feature screening for semi-competing risks outcomes with application to breast cancer data. Statistical Methods in Medical Research, 30(11), 2428-2446. https://dx.doi.org/10.1177/09622802211037071 0962-2802 https://hdl.handle.net/10356/157027 10.1177/09622802211037071 34519231 2-s2.0-85114854933 11 30 2428 2446 en RG98/20 Statistical Methods in Medical Research © 2021 The Author(s). All rights reserved. This paper was published in Statistical Methods in Medical Research and is made available with permission of The Author(s). application/pdf |
| spellingShingle | Science::Mathematics Gene Expression Data Joint Survival Function Peng, Mengjiao Xiang, Liming Correlation-based joint feature screening for semi-competing risks outcomes with application to breast cancer data |
| title | Correlation-based joint feature screening for semi-competing risks outcomes with application to breast cancer data |
| title_full | Correlation-based joint feature screening for semi-competing risks outcomes with application to breast cancer data |
| title_fullStr | Correlation-based joint feature screening for semi-competing risks outcomes with application to breast cancer data |
| title_full_unstemmed | Correlation-based joint feature screening for semi-competing risks outcomes with application to breast cancer data |
| title_short | Correlation-based joint feature screening for semi-competing risks outcomes with application to breast cancer data |
| title_sort | correlation based joint feature screening for semi competing risks outcomes with application to breast cancer data |
| topic | Science::Mathematics Gene Expression Data Joint Survival Function |
| url | https://hdl.handle.net/10356/157027 |
| work_keys_str_mv | AT pengmengjiao correlationbasedjointfeaturescreeningforsemicompetingrisksoutcomeswithapplicationtobreastcancerdata AT xiangliming correlationbasedjointfeaturescreeningforsemicompetingrisksoutcomeswithapplicationtobreastcancerdata |