UCLA

When estimating the effects of medical therapies from their use outside of randomized trials, researchers often rely on assumptions that are difficult to justify and typically impossible to verify. The resulting estimates may thus be far from their intended causal targets, potentially making a harmful treatment appear beneficial or vice versa. We review the stability-controlled quasi-experiment (SCQE), a method suited to settings where a treatment's prevalence changes sharply over a short period, and apply it to assess the effects of remdesivir, hydroxychloroquine, and dexamethasone on COVID-19 mortality. Rather than requiring debate about the absence (or limited strength) of unobserved confounding, about "parallel trends'', or other well-known strategies, the SCQE asks users to reason about a "baseline trend'' assumption. In this setting, this asks"How much could COVID-19 mortality have changed over a short period, absent the treatment change in question?'' Any plausible range for this assumption yields a corresponding range of plausible causal effect estimates. Conversely, SCQE clarifies what baseline trends must be defended or refuted in order to defend or refute a given conclusion about a treatment's efficacy or harm. Using data from two hospital systems early in the COVID-19 pandemic, we show that SCQE could have enabled safe yet informative inferences about treatment effects before clinical trial completion, producing conclusions consistent with eventual trial results.