A trending approach for analysis in critical care research: Bayesian statistics.

Biostatistics plays an essential role in medical research. In the 20th century, frequentist statistics dominated clinical investigations. In this approach, inference is based on the probability of obtaining the observed data, assuming that the null hypothesis is either accepted or rejected. The Bayesian statistical inference is considerably different. It involves three main components: prior probability, likelihood of observed data, and posterior probability. Priors can enhance interpretation by incorporating existing knowledge. The posterior distribution provides rich interpretive value and uses probability distributions rather than single p-values. The ANDROMEDA-SHOCK trial, originally analyzed with frequentist methods, showed a clinically meaningful but not statistically significant mortality reduction with peripheral perfusion-targeted resuscitation compared to lactate-targeted therapy. A Bayesian reanalysis by Zampieri et al. revealed a consistently high posterior probability (over 90 %) that peripheral perfusion-targeted resuscitation reduces mortality at both 28 and 90 days, providing stronger evidence of its benefit. Sidebotham et al. suggested that low participant susceptibility may reduce trial power, despite effective interventions. They modeled scenarios to estimate the proportion of statistically significant results under various assumptions. Hatton et al. showed that Bayesian reasoning can improve surgical judgment by offering clearer estimates of benefit and harm. Even a well-conducted RCT on REBOA in trauma was analyzed using a Bayesian approach. This highlights the growing expectation that Bayesian statistics will be increasingly used in original investigations during the second quarter of the 21st century. Its integration into clinical research improves the interpretability of the results and bridges the gap between statistical inference and clinical reasoning.