Pitfalls and perils of survival analysis under incorrect assumptions: the case of COVID-19 data

Abstract

Non-parametric survival analysis has become a very popular statistical method in current medical research. However, resorting to survival analysis when its fundamental assumptions are not fulfilled can severely bias the results. Currently, hundreds of clinical studies are using survival methods to investigate factors potentially associated with the prognosis of coronavirus disease 2019 (COVID-19) and test new preventive and therapeutic strategies. In the pandemic era, it is more critical than ever to base decision-making on evidence and rely on solid statistical methods, but this is not always the case. Serious methodological errors have been identified in recent seminal studies about COVID-19: One reporting outcomes of patients treated with remdesivir and another one on the epidemiology, clinical course, and outcomes of critically ill patients. High-quality evidence is essential to inform clinicians about optimal COVID-19 therapies and policymakers about the true effect of preventive measures aiming to tackle the pandemic. Though timely evidence is needed, we should encourage the appropriate application of survival analysis methods and careful peer-review to avoid publishing flawed results, which could affect decision-making. In this paper, we recapitulate the basic assumptions underlying non-parametric survival analysis and frequent errors in its application and discuss how to handle data on COVID-19.

Keywords

• coronavirus infections
• betacoronavirus
• severe acute respiratory syndrome
• survival analysis
• data interpretation, statistical