Real-world comparison of counterfactual inference methods for evaluating impact of antidepressants on COVID outcomes

In order to discover drugs that could be repurposed for a public health emergency like the COVID-19 pandemic, the National COVID Cohort Collaborative (N3C) database compiles health records including 22 millions individuals and 8.9 million cases of COVID-19. Here, we sought to use this data to systematically investigate whether antidepressants could impact COVID-19 outcomes, adjusting for known risk factors for severe outcome. We conducted large scale target trial emulation, comparing all pairs of 18 antidepressants to each other. Because the best approach for discovering such drug effects from observational data is not known, we applied a series of methods for identifying drug effects by estimating the counterfactual outcome that would be observed in a randomized trial. We found that all methods for counterfactual outcome estimation were prone to bias due to poorly controlled unmeasured confounding. We describe this bias, which appears to be induced partly by conditioning on treatment exposure, opening a back door path, via collider effects, between treatment and exposure via unmeasured confounders. Via empirical simulations, we show that our approach is able to detect this bias. In result, we state that we are not able to confidently identify any antidepressant impacting COVID-19 outcomes.