Framework to aid analysis and interpretation of ongoing COVID-19 research
Thomas Yates, Francesco Zaccardi, Cameron Razieh, Clare Gillies, Alex V. Rowlands, David E. Kloecker, Yogini Chudasama, Melanie J. Davies, Kamlesh Khunti
Abstract
<ns4:p>The global coronavirus pandemic has precipitated a rapid unprecedented research response, including investigations into risk factors for COVID-19 infection, severity, or death. However, results from this research have produced heterogeneous findings, including articles published in Wellcome Open Research. Here, we use ethnicity, obesity, and smoking as illustrative examples to demonstrate how a research question can produce very different answers depending on how it is framed. For example, these factors can be both strongly associated or have a null association with death due to COVID-19, even when using the same dataset and statistical modelling. Highlighting the reasons underpinning this apparent paradox provides an important framework for reporting and interpreting ongoing COVID-19 research.</ns4:p>