Bayesian statistics in anesthesia practice: a tutorial for anesthesiologists.
Anesthetic pharmacology
Bayesian
Clinical research
Statistics
Journal
Journal of anesthesia
ISSN: 1438-8359
Titre abrégé: J Anesth
Pays: Japan
ID NLM: 8905667
Informations de publication
Date de publication:
04 2022
04 2022
Historique:
received:
12
05
2021
accepted:
25
01
2022
pubmed:
12
2
2022
medline:
2
4
2022
entrez:
11
2
2022
Statut:
ppublish
Résumé
This narrative review intends to provide the anesthesiologist with the basic knowledge of the Bayesian concepts and should be considered as a tutorial for anesthesiologists in the concept of Bayesian statistics. The Bayesian approach represents the mathematical formulation of the idea that we can update our initial belief about data with the evidence obtained from any kind of acquired data. It provides a theoretical framework and a statistical method to use pre-existing information within the context of new evidence. Several authors have described the Bayesian approach as capable of dealing with uncertainty in medical decision-making. This review describes the Bayes theorem and how it is used in clinical studies in anesthesia and critical care. It starts with a general introduction to the theorem and its related concepts of prior and posterior probabilities. Second, there is an explanation of the basic concepts of the Bayesian statistical inference. Last, a summary of the applicability of some of the Bayesian statistics in current literature is provided, such as Bayesian analysis of clinical trials and PKPD modeling.
Identifiants
pubmed: 35147768
doi: 10.1007/s00540-022-03044-9
pii: 10.1007/s00540-022-03044-9
pmc: PMC8967750
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
294-302Informations de copyright
© 2022. The Author(s).
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