Bayesian statistics in anesthesia practice: a tutorial for anesthesiologists

Michele Introna, Johannes P van den Berg*, Douglas J Eleveld, Michel M R F Struys

*Corresponding author for this work

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Abstract

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.

Original languageEnglish
Pages (from-to)294–302
Number of pages9
JournalJournal of anesthesia
Volume36
Early online date11-Feb-2022
DOIs
Publication statusPublished - Apr-2022

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