Comparison of the performance of linear discriminant analysis and binary logistic regression applied to risk factors for mortality in Ebola virus disease patients

Abstract

Our study aimed to identify risk factors associated with mortality in Ebola patients using binary logistic regression analysis and linear discriminant analysis, and to assess the predictive power of these two methods. Our study was a randomized, double-blind, controlled (observational) clinical trial conducted in 2018 during the 10th Ebola outbreak in eastern DRC. The study included 363 patients divided into two treatment arms, including 182 patients treated with MAB114 (Ebang) and 181 patients treated with REGENERON (REGN-EB3 ). After a thorough analysis of the data, both statistical analysis methods selected the same set of variables (risk factors) for the binary logistic regression we obtained: viral load 0.58 (0.5-0.67), creatinine 1.98 (1.58-0.67) and aspartate aminotransferase 0.99 (0.9-1); as for the linear discriminant analysis we have viral load (0.88), creatinine (0.94) and aspartate aminotransferase (0.78). We also see almost the same results when different prediction probabilities are evaluated. Logistic regression predicted a mortality rate of 36.5% and linear discriminant analysis predicted a mortality rate of 38.8%. Using the AUC (area under the curve) score, we were able to evaluate two methods and obtain a score of 0.935 for the binary logistic regression and 0.932 for the linear discriminant analysis. According to the evaluation hypothesis, both methods give the same risk factors (viral load (Ctnp), creatinine and alanine aminotransferase (ALT)) with a probability of 93%.