Corticosteroids in COVID-19: Optimizing Observational Research through Target Trial Emulations.
Article
Overview
abstract
BACKGROUND: Observational research provides a unique opportunity to learn causal effects when randomized trials are unavailable, but obtaining the correct estimates hinges on a multitude of design and analysis choices. We illustrate the advantages of modern causal inference methods and compare to standard research practice to estimate the effect of corticosteroids on mortality in hospitalized COVID-19 patients in an observational dataset. We use several large RCTs to benchmark our results. METHODS: Our retrospective data consists of 3,298 COVID-19 patients hospitalized at New York-Presbyterian March 1-May 15, 2020. We design our study using the target trial framework. We estimate the effect of an intervention consisting of six days of corticosteroids administered at the time of severe hypoxia and contrast with an intervention consisting of no corticosteroids. The dataset includes dozens of time-varying confounders. We estimate the causal effects using a doubly robust estimator where the probabilities of treatment, outcome, and censoring are estimated using flexible regressions via super learning. We compare these analyses to standard practice in clinical research, consisting of two approaches: (i)Cox models for an exposure of corticosteroids receipt within various time windows of hypoxia, and (ii)Cox time-varying model where the exposure is daily administration of corticosteroids beginning at hospitalization. RESULTS: Our target trial emulation estimates corticosteroids to reduce 28-day mortality from 32% (95% confidence interval: 31-34) to 23% (21-24). This is qualitatively identical to the WHO's RCT meta-analysis result. Hazard ratios from the Cox models range in size and direction from 0.50 (0.41-0.62) to 1.08 (0.80-1.47) and all study designs suffer from various forms of bias. CONCLUSION: We demonstrate that clinical research based on observational data can unveil true causal relations. However, the correctness of these effect estimates requires designing and analyzing the data based on principles which are different from the current standard in clinical research. Widespread communication and adoption of these design and analytical techniques is of high importance for the improvement of clinical research.
