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Statistical Analysis of Clinical COVID-19 Data: A Concise Overview of Lessons Learned, Common Errors and How to Avoid Them.
Wolkewitz, Martin; Lambert, Jerome; von Cube, Maja; Bugiera, Lars; Grodd, Marlon; Hazard, Derek; White, Nicole; Barnett, Adrian; Kaier, Klaus.
  • Wolkewitz M; Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Lambert J; Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • von Cube M; Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Bugiera L; Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Grodd M; Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Hazard D; Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • White N; School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia.
  • Barnett A; School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia.
  • Kaier K; Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Clin Epidemiol ; 12: 925-928, 2020.
Article in English | MEDLINE | ID: covidwho-781765
Semantic information from SemMedBD (by NLM)
1. Hospitalization AFFECTS risk factors
Subject
Hospitalization
Predicate
AFFECTS
Object
risk factors
2. COVID-19 PROCESS_OF Patients
Subject
COVID-19
Predicate
PROCESS_OF
Object
Patients
3. Hospitalization AFFECTS risk factors
Subject
Hospitalization
Predicate
AFFECTS
Object
risk factors
4. COVID-19 PROCESS_OF Patients
Subject
COVID-19
Predicate
PROCESS_OF
Object
Patients
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

Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study / Risk factors Language: English Journal: Clin Epidemiol Year: 2020 Document Type: Article Affiliation country: Clep.s256735

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study / Risk factors Language: English Journal: Clin Epidemiol Year: 2020 Document Type: Article Affiliation country: Clep.s256735