Transfusion Strategies and the Pragmatic Appeal of Bayesian Analysis for Trauma Research

Article Medicine, General & Internal

Use of Bayesian Statistics to Reanalyze Data From the Pragmatic Randomized Optimal Platelet and Plasma Ratios Trial

Daniel Lammers et al.

Summary: In this study, a post hoc bayesian analysis of the PROPPR Trial found evidence supporting mortality reduction with a balanced resuscitation strategy for patients in hemorrhagic shock. Bayesian statistical methods offer probability-based results capable of direct comparison between various interventions and should be considered for future studies assessing trauma-related outcomes.

JAMA NETWORK OPEN (2023)

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Article Critical Care Medicine

Using Bayesian Methods to Augment the Interpretation of Critical Care Trials An Overview of Theory and Example Reanalysis of the Alveolar Recruitment for Acute Respiratory Distress Syndrome Trial

Fernando G. Zampieri et al.

Summary: Most randomized trials are traditionally analyzed using frequentist statistical approaches like null hypothesis testing and P values. However, the concept of P values can be difficult to understand and may lead to misinterpretation. Bayesian methods offer an alternative probabilistic interpretation of data, providing a distribution of effect sizes compatible with observed trial data based on prior assumptions. Bayesian reanalysis, although increasingly common, raises concerns about potential bias if priors are not predefined. Standardization of priors in Bayesian reanalysis of clinical trials may be beneficial for the critical care community.

AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE (2021)

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Review Critical Care Medicine

Clinical trials in critical care: can a Bayesian approach enhance clinical and scientific decision making?

Christopher J. Yarnell et al.

Summary: Recent Bayesian reanalyses of prominent trials in critical illness have generated controversy by contradicting the initial conclusions based on conventional frequentist analyses. In most cases, Bayesian and frequentist analyses agreed, but Bayesian analysis identified interventions where benefit was probable despite the absence of statistical significance. Bayesian analysis in critical care medicine can help to distinguish harm from uncertainty and establish the probability of clinically important benefit for clinicians, policy makers, and patients.

LANCET RESPIRATORY MEDICINE (2021)

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Article Medicine, General & Internal