A0422
Title: Envelope-based partial partial least squares with application to cytokine-based biomarker analysis for COVID-19
Authors: Yeonhee Park - University of Wisconsin (United States) [presenting]
Zhihua Su - University of Florida (United States)
Dongjun Chung - Ohio State University (United States)
Abstract: 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 PLS regression practice is treating the categorical variable as continuous. However, studies find that this practice may lead to biased estimates and invalid inferences. Based on a connection between the envelope model and PLS, an envelope-based partial PLS estimator is developed 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. A numerical study shows that this approach can achieve more efficiency gains in estimation and produce better predictions. The method is applied for identifying 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.
