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. 2020 Sep 3:12:925-928.
doi: 10.2147/CLEP.S256735. eCollection 2020.

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

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 Kaier  1
Affiliations

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

Martin Wolkewitz et al. Clin Epidemiol. .

Abstract

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

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Conflict of interest statement

The authors report no conflicts of interest for this work.

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