The evidence of Bayesian A/B testing in the contrast of clinical events by COVID-19

Cómo citar este artículo: C. Ramos-Vera. The evidence of Bayesian A/B testing in the contrast of clinical events by COVID-19. Infectio 2022; 26(1): 99-100 Sr. Editor: The clinical investigations reported in this journal employ the standard framework of frequentist statistics based on significance assumptions (p < 0.05). This method leads to a dichotomization of the results as “significant” or “non-significant” requiring the evaluation of statistical hypotheses1. Therefore, the use of the Bayesian approach is important as an improved way of drawing statistical conclusions from clinical data since it facilitates the answer to the question, what is the probability that the effect is conclusive based on the data, which provides greater validity to the significant conclusions. One of the best known methods is the Bayes factor (FB), which estimates the probability of one hypothesis relative to the other given the data (e.g., null hypothesis vs alternate hypothesis)1,2, this allows estimation of the weight of evidence (10 times the decimal logarithm value of the FB)3,4, useful for decision making of significant findings, where results with evidence values greater than 20 are optimal for clinical decision making.