Bayesian principles and concepts and their applications in medicine

Résumé 0

The medical literature extensively uses statistical methods, particularly p-values obtained after null hypothesis testing. These tools determine whether two proportions differ by dichotomizing the p-value on either side of an alpha threshold, typically set at 0.05, based on data collected from a sample of subjects. In addition to these conventional methods, Bayesian statistical analyses have emerged in the literature over the past 30 years. These methods differ from conventional ones in that they do not rely on p-values and instead use a subjectivist definition of probability. This results in the expression of an a priori distribution for the parameter to be estimated, which specifies the available knowledge about the parameter before the experiment is carried out. This a priori distribution, combined with the probability of the data observed in the sample, updates our knowledge of the parameter of interest (difference in proportion or odds ratio). The principles and concepts underlying Bayesian methods are presented here, along with a few examples of their application, particularly in reproductive medicine, to show the advantages of these methods over conventional methods.