Statistical Analysis of Clinical COVID-19 Data: A Concise Overview of Lessons Learned, Common Errors and How to Avoid Them

Abstract

Martin Wolkewitz,1 Jerome Lambert,1 Maja von Cube,1 Lars Bugiera,1 Marlon Grodd,1 Derek Hazard,1 Nicole White,2 Adrian Barnett,2 Klaus Kaier1 1Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; 2School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, AustraliaCorrespondence: Klaus KaierInstitute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, GermanyEmail [email protected]: By definition, in-hospital patient data are restricted to the time between hospital admission and discharge (alive or dead). For hospitalised cases of COVID-19, a number of events during hospitalization are of interest regarding the influence of risk factors on the likelihood of experiencing these events. The same is true for predicting times from hospital admission of COVID-19 patients to intensive care or from start of ventilation (invasive or non-invasive) to extubation. This logical restriction of the data to the period of hospitalisation is associated with a substantial risk that inappropriate methods are used for analysis. Here, we briefly discuss the most common types of bias which can occur when analysing in-hospital COVID-19 data.Keywords: competing risk bias, immortal-time bias, competing events, time-dependent bias, time-varying exposure, time-to-event analysis

Keywords

• competing risk bias
• immortal-time bias
• competing events
• time-dependent bias
• time-varying exposure
• time-to-event analysis