A resample-replace lasso procedure for combining high-dimensional markers with limit of detection. Issue 16 (10th December 2022)

• Record Type: Journal Article
• Authors: Wang, Jinjuan; Zhao, Yunpeng; Tang, Larry L.; Mueller, Claudius; Li, Qizhai
• Is Part Of: Journal of applied statistics. Volume 49:Issue 16(2022)
• Page Start: 4278
• Page End: 4293
• Publication Date: 2022-12-10
• Subjects: Limit of detection (LOD) -- graphical lasso -- precision matrix -- area under the receiver operating characteristic curve (AUC) -- high-dimensional data -- imputation; 97K80; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2021.1977785
• Languages: English
• ISSNs: 0266-4763

Abstract

Abstract : 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.