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.