Envelope-based partial partial least squares with application to cytokine-based biomarker analysis for COVID-19.
Envelope-based partial partial least squares with application to cytokine-based biomarker analysis for COVID-19.
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DOI：
10.1002/sim.9526
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发表时间：
2022-10-15
影响因子：
2
通讯作者：
Chung, Dongjun
中科院分区：
文献类型：
--
作者：
Park, Yeonhee;Su, Zhihua;Chung, Dongjun
关键词：
偏最小二乘（PLS）回归是一种流行的替代普通最小二乘回归，因为它的上级预测性能在许多情况下证明。在各种当代应用中，预测因子包括连续变量和分类变量。PLS回归中的一个常见做法是将分类变量视为连续变量。然而，研究发现，这种做法可能会导致有偏见的估计和无效的推论（Schuberth等人，2018年）。基于包络模型和PLS之间的联系，我们开发了一种基于包络的偏PLS估计量，该估计量考虑了响应的条件分布和分类预测变量的连续预测变量的PLS回归。对于该估计量，建立了Root-n一致性和渐近正态性。数值研究表明，该方法可以获得更高的估计效率，并产生更好的预测。该方法用于鉴定COVID-19患者的基于细胞因子的生物标志物，其揭示了基于细胞因子的生物标志物与患者的临床信息（包括入院时的疾病状态和人口统计学特征）之间的关联。有效的估计导致一个明确的科学解释的结果。
Partial least squares (PLS) regression is a popular alternative to ordinary least squares regression because of its superior prediction performance demonstrated in many cases. In various contemporary applications, the predictors include both continuous and categorical variables. A common practice in PLS regression is to treat the categorical variable as continuous. However, studies find that this practice may lead to biased estimates and invalid inferences (Schuberth et al., 2018). Based on a connection between the envelope model and PLS, we develop an envelope‐based partial PLS estimator that considers the PLS regression on the conditional distributions of the response(s) and continuous predictors on the categorical predictors. Root‐n consistency and asymptotic normality are established for this estimator. Numerical study shows that this approach can achieve more efficiency gains in estimation and produce better predictions. The method is applied for the identification of cytokine‐based biomarkers for COVID‐19 patients, which reveals the association between the cytokine‐based biomarkers and patients' clinical information including disease status at admission and demographical characteristics. The efficient estimation leads to a clear scientific interpretation of the results.
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影响因子：
18.2
作者：
Hair, Joe F.;Sarstedt, Marko;Mena, Jeannette A.
通讯作者：
Mena, Jeannette A.
影响因子：
--
作者：
Schuberth, Florian;Henseler, Jorg;Dijkstra, Theo K.
通讯作者：
Dijkstra, Theo K.
影响因子：
2.5
作者：
Cook, R. Dennis;Su, Zhihua
通讯作者：
Su, Zhihua
DOI：
10.1080/01621459.2014.983235
发表时间：
2015-06-01
影响因子：
3.7
作者：
Cook, R. Dennis;Zhang, Xin
通讯作者：
Zhang, Xin
影响因子：
4.5
作者：
Khare, Kshitij;Pal, Subhadip;Su, Zhihua
通讯作者：
Su, Zhihua