Bias in early coronavirus disease 2019 research

Abstract

In the context of the ongoing global pandemic of coronavirus disease 2019 (COVID-19), most scientific evidence related to disease transmission and clinical outcomes, especially in the first wave, originated from observational studies. Those studies have provided a basic understanding of various aspects of this disease, including clinical manifestations, pathogenesis, diagnosis, and treatment. However, the accuracy and credibility of some such studies have been questioned because of the presence of bias, which is occasionally addressed in the published research. In this review, the principal types of bias in COVID-19 research are discussed, namely selection and misclassification bias. For this mini literature search, Medline database had used to identify the related articles. Many studies have shown some selection bias in sampling their population, leading to an over-or underestimation of true results. Understanding the effect of bias in the context of COVID-19, research is important for two reasons. First, it enables a discussion of the findings of such biases, especially those that occurred in studies contributing to evidence-based medicine. Second, it helps ensure that researchers avoid such bias in future research and any subsequent infectious pandemic. The key points in avoiding such bias are study design and the need for care in collecting information on both exposure and outcome; however, in the real world, these are very challenging matters.