Non-parametric survival analysis has become a very popular statistical method in current medical research. Employing, however, survival methodology 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 Corona Virus Disease 2019 (Covid-19), and test new preventive and therapeutic strategies. In the pandemic era, it is more critical than ever that decision-making is evidence-based and relies on solid statistical methods. However, this is not always the case. Serious methodologic 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 of Covid-19.
Determinants of Length of Stay: A Parametric Survival Analysis
