Biomarkers are often measured in bulk to diagnose patients, monitor patient conditions, and research novel drug pathways. The measurement of these biomarkers often suffers from detection limits that result in missing and untrustworthy measurements. Frequently, missing biomarkers are imputed so that down-stream analysis can be conducted with modern statistical methods that cannot normally handle data subject to informative censoring. This work develops an empirical Bayes $g$-modeling method for imputing and denoising biomarker measurements. We establish superior estimation properties compared to popular methods in simulations and demonstrate the utility of the estimated biomarker measurements for down-stream analysis.

翻译：生物标志物常被批量测量以诊断患者、监测病情并研究新型药物通路。这些生物标志物的测量常受限于检测阈值，导致数据缺失或测量值不可靠。通常需对缺失的生物标志物进行插补，以便采用现代统计方法进行下游分析——这类方法通常无法处理存在信息删失的数据。本研究提出一种基于经验贝叶斯$g$-建模的方法，用于生物标志物测量值的插补与去噪。通过仿真实验证明，该方法相较于主流方法具有更优的估计性能，并展示了估算生物标志物测量值在下游分析中的实用价值。