https://socictopen.socict.org/files/original/7ad1518bb2798ba471f1d17cd07e568c.pdf d353a842d45e769e4265fca7bdf3c72d Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Statistical Analysis of Clinical COVID-19 Data: A Concise Overview of Lessons Learned, Common Errors and How to Avoid Them Creator An entity primarily responsible for making the resource Wolkewitz M, Lambert J, von Cube M, Bugiera L, Grodd M, Hazard D, White N, Barnett A, Kaier K Description An account of the resource 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 kaier@imbi.uni-freiburg.deAbstract: 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 Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource immortal time bias, Time dependent bias, Competing events, time-varying exposure, competing risk bias, time-to-event analysis Source A related resource from which the described resource is derived Biotemas Publisher An entity responsible for making the resource available Universidade Federal de Santa Catarina Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Infectious and parasitic diseases