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The usual development cycles are too slow for the development of vaccines,diagnostics and treatments in pandemics such as the ongoing SARS-CoV-2pandemic. Given the pressure in such a situation, there is a risk that findingsof early clinical trials are overinterpreted despite their limitations in termsof size and design. Motivated by a non-randomized open-label studyinvestigating the efficacy of hydroxychloroquine in patients with COVID-19, wedescribe in a unified fashion various alternative approaches to the analysis ofnon-randomized studies. A widely used tool to reduce the impact oftreatment-selection bias are so-called propensity score (PS) methods.Conditioning on the propensity score allows one to replicate the design of arandomized controlled trial, conditional on observed covariates. Extensionsinclude the g-computation approach, which is less frequently applied, inparticular in clinical studies. Moreover, doubly robust estimators provideadditional advantages. Here, we investigate the properties of propensity scorebased methods including three variations of doubly robust estimators in smallsample settings, typical for early trials, in a simulation study. R code forthe simulations is provided.

Causal inference methods for small non-randomized studies: Methods and an application in COVID-19