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

ABSTRACT: The usual development cycles are too slow for the development of vaccines, diagnostics and treatments in pandemics such as the ongoing SARS-CoV-2 pandemic. Given the pressure in such a situation, there is a risk that findings of early clinical trials are overinterpreted despite their limitations in terms of size and design. Motivated by a non-randomized open-label study investigating the efficacy of hydroxychloroquine in patients with COVID-19, we describe in a unified fashion various alternative approaches to the analysis of non-randomized studies. A widely used tool to reduce the impact of treatment-selection bias are so-called propensity score (PS) methods. Conditioning on the propensity score allows one to replicate the design of a randomized controlled trial, conditional on observed c

SUBMITTER: Friedrich S 

PROVIDER: S-EPMC7834813 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature