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|a 10.1080/02664763.2021.1977785
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|a eng
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| 100 |
1 |
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|a Wang, Jinjuan
|e verfasserin
|4 aut
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| 245 |
1 |
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|a A resample-replace lasso procedure for combining high-dimensional markers with limit of detection
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 11.11.2022
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|a published: Electronic-eCollection
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|a Citation Status PubMed-not-MEDLINE
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|a © 2021 Informa UK Limited, trading as Taylor & Francis Group.
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|a In disease screening, a biomarker combination developed by combining multiple markers tends to have a higher sensitivity than an individual marker. Parametric methods for marker combination rely on the inverse of covariance matrices, which is often a non-trivial problem for high-dimensional data generated by modern high-throughput technologies. Additionally, another common problem in disease diagnosis is the existence of limit of detection (LOD) for an instrument - that is, when a biomarker's value falls below the limit, it cannot be observed and is assigned an NA value. To handle these two challenges in combining high-dimensional biomarkers with the presence of LOD, we propose a resample-replace lasso procedure. We first impute the values below LOD and then use the graphical lasso method to estimate the means and precision matrices for the high-dimensional biomarkers. The simulation results show that our method outperforms alternative methods such as either substitute NA values with LOD values or remove observations that have NA values. A real case analysis on a protein profiling study of glioblastoma patients on their survival status indicates that the biomarker combination obtained through the proposed method is more accurate in distinguishing between two groups
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|a Journal Article
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4 |
|a 97K80
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| 650 |
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|a Limit of detection (LOD)
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| 650 |
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4 |
|a area under the receiver operating characteristic curve (AUC)
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| 650 |
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4 |
|a graphical lasso
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| 650 |
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4 |
|a high-dimensional data
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| 650 |
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|a imputation
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| 650 |
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4 |
|a precision matrix
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| 700 |
1 |
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|a Zhao, Yunpeng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tang, Larry L
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Mueller, Claudius
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Qizhai
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Journal of applied statistics
|d 1991
|g 49(2022), 16 vom: 01., Seite 4278-4293
|w (DE-627)NLM098188178
|x 0266-4763
|7 nnas
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| 773 |
1 |
8 |
|g volume:49
|g year:2022
|g number:16
|g day:01
|g pages:4278-4293
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| 856 |
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|u http://dx.doi.org/10.1080/02664763.2021.1977785
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