A pragmatic approach to estimating average treatment effects from EHR data: the effect of prone positioning on mechanically ventilated COVID-19 patients
This addresses the problem of providing clinicians with preliminary guidance on treatment strategies when randomized trial data is unavailable, but it appears incremental as it builds on existing causal inference approaches.
The authors tackled the lack of agreed-upon methods for estimating treatment effects from observational data by proposing a pragmatic methodology, applying it to estimate the effect of prone positioning on COVID-19 ICU patients, though no concrete numerical results are provided in the abstract.
Despite the recent progress in the field of causal inference, to date there is no agreed upon methodology to glean treatment effect estimation from observational data. The consequence on clinical practice is that, when lacking results from a randomized trial, medical personnel is left without guidance on what seems to be effective in a real-world scenario. This article proposes a pragmatic methodology to obtain preliminary but robust estimation of treatment effect from observational studies, to provide front-line clinicians with a degree of confidence in their treatment strategy. Our study design is applied to an open problem, the estimation of treatment effect of the proning maneuver on COVID-19 Intensive Care patients.