Comparison of a Target Trial Emulation Framework to Cox Regression to Estimate the Effect of Corticosteroids on...

Communication and adoption of modern study design and analytical techniques is of high importance for the improvement of clinical research from observational data.

To compare (1) a modern method for causal inference including a target trial emulation framework and doubly robust estimation to (2) approaches common in the clinical literature such as Cox proportional hazards models. To do this, we estimate the effect of corticosteroids on mortality for moderate-to-severe coronavirus disease 2019 (COVID-19) patients. We use the World Health Organization's (WHO) meta-analysis of corticosteroid randomized controlled trials (RCTs) as a benchmark.

Retrospective cohort study using longitudinal electronic health record data for 28 days from time of hospitalization.

Multi-center New York City hospital system.

Adult patients hospitalized between March 1-May 15, 2020 with COVID-19 and not on corticosteroids for chronic use.

Corticosteroid exposure defined as >0.5mg/kg methylprednisolone equivalent in a 24-hour period. For target trial emulation, interventions are (1) corticosteroids for six days if and when patient meets criteria for severe hypoxia and (2) no corticosteroids. For approaches common in clinical literature, treatment definitions used for variables in Cox regression models vary by study design (no time frame, one-, and five-days from time of severe hypoxia).

28-day mortality from time of hospitalization.

3,298 patients (median age 65 (IQR 53-77), 60% male). 423 receive corticosteroids at any point during hospitalization, 699 die within 28 days of hospitalization. Target trial emulation estimates corticosteroids to reduce 28-day mortality from 32.2% (95% CI 30.9-33.5) to 25.7% (24.5-26.9). This estimate is qualitatively identical to the WHO's RCT meta-analysis odds ratio of 0.66 (0.53-0.82)). Hazard ratios using methods comparable to current corticosteroid research range in size and direction from 0.50 (0.41-0.62) to 1.08 (0.80-1.47).

Clinical research based on observational data can unveil true causal relationships; however, the correctness of these effect estimates requires designing the study and analyzing the data based on principles which are different from the current standard in clinical research.