Binary Logistic Regression and Linear Discriminant Analyses in Evaluating Laboratory Factors Associated with COVID-19: A Comparison of Two Statistical Methods

Abstract

Introduction: Few studies have been conducted to construct a reliable predictive model for the differential diagnosis of severe and non-severe Coronavirus disease-2019 (COVID-19) in the early stages of the disease. This study aimed to compare the accuracy of linear discriminate analysis (LDA) and binary logistic regression (BLR), as two empirical correlations, in predicting COVID-19 severity using single laboratory data and calculated indexes such as the neutrophil-to-lymphocyte ratio (NLR) and systemic immune-inflammation index (SII). Materials and Methods: We investigated 109 patients with confirmed COVID-19 pneumonia. Epidemiological, demographic, clinical, laboratory, and outcome data were obtained, and the patients were classified into two groups: mild group (42 patients) and severe group (67 patients). Results: A comparison of the clinical data in the severe and non-severe groups showed significant differences in SpO2 and respiratory rate. In addition, significant difference in NLR, SII, white blood cell count, neutrophil count, mean corpuscular volume and mean corpuscular hemoglobin, lymphocyte count, erythrocyte sedimentation rate, lactate dehydrogenase, and blood urea nitrogen was found between both groups. Moreover, there was a small difference between the LDA and LR models, and LDA was more appropriate for a smaller sample size. Conclusion: Our predictive models could help clinicians to identify patients at risk of severe COVID-19 Such prediction can be performed by a simple blood test. LDA and BLR can be used to effectively classify patients with severe and non-severe COVID-19, even with violation of the normality assumption.

Keywords

• severe covid-19
• linear discriminant analysis
• binary logistic regression
• blood test data