Estimating the causal effect of dexamethasone versus hydrocortisone on the neutrophil- lymphocyte ratio in critically ill COVID-19 patients from Tygerberg Hospital ICU using TMLE method

Abstract

Background: Causal inference from observational studies is an area of interest to researchers, advancing rapidly over the years and with it, the methods for causal effect estimation. Among them, Targeted Maximum Likelihood estimation (TMLE) possesses arguably the most outstanding statistical properties, and with no outright treatment for COVID-19, there was an opportunity to estimate the causal effect of dexamethasone versus hydrocortisone upon the neutrophil-lymphocyte ratio (NLR), a vital indicator for disease progression among critically ill COVID-19 patients. Methods: TMLE variations were used in the analysis. Super Learner (SL), Bayesian Additive Regression Trees (BART) and parametric regression (PAR) were implemented to estimate the average treatment effect (ATE). Results: The study had 168 participants, 128 on dexamethasone and 40 on hydrocortisone. The mean causal difference in NLR on day 5; ATE [95% CI]: from SL-TMLE was − 0.309 [-3.800, 3.182] BART-TMLE 0.246 [-3.399, 3.891] and PAR-TMLE 1.245 [-1.882, 4372]. The ATE of dexamethasone versus hydrocortisone on NLR was not statistically significant since the confidence interval included zero. Conclusion: The effect of dexamethasone is not significantly different from that of hydrocortisone on NLR in critically ill COVID-19 patients admitted to ICU. This implies that the difference in effect on NLR between the two drugs is due to random chance. TMLE remains an outstanding approach for causal analysis of observational studies with the ability to be augmented with multiple prediction approaches.