Advancing the Design, Analysis, and Interpretation of Acute Respiratory Distress Syndrome Trials Using Modern Statistical Tools

Acute respiratory distress syndrome (ARDS) is a common condition of severe respiratory failure that can be caused by a variety of illnesses (e.g., Covid-19 and other respiratory viruses, sepsis, and pneumonia), with hospital mortality of 30-40% and significant morbidity among survivors. ARDS randomized clinical trials (RCTs) have been hampered by (1) analyses that yield a strict binary conclusion concerning treatment efficacy, (2) inadequate statistical methods to assess heterogeneity of treatment effect among varying patient types, and (3) death-truncated non-mortality outcomes such as length of stay.

This research seeks to offer more intuitive probabilistic interpretations of an intervention’s efficacy and maximally leverage the information learned from a trial. The researchers will apply a suite of Bayesian causal inference and machine learning methods to 29 international and NIH/NHLBI-funded multicenter ARDS RCTs to (1) determine the probability that the tested interventions produce benefit or harm on the absolute and relative scale for both mortality and non-mortality clinical outcomes, (2) quantify the degree of confidence in these conclusions, and (3) identify clinically meaningful subgroups most likely to benefit.

National Heart, Lung, and Blood Institute