## Introduction

The Pragmatic Randomized Optimal Platelet and Plasma Ratios (PROPPR) trial, published in 2015, was a landmark clinical trial that compared two different trauma transfusion strategies—a ‘1:1:1’ ratio and a ‘1:1:2’ ratio of transfused units of plasma, platelets and red blood cells. This study demonstrated a 4.2% and 3.7% reduction in mortality at 24 hours and at 30 days within the 1:1:1 transfusion cohort, respectively. These differences, however, were not statistically significant at either of the co-primary end points (p=0.12 and p=0.26, respectively) and the null hypothesis—that there was no treatment effect based on the transfusion strategy used, that is, no mortality benefit—could not be rejected at the level of p<0.05.1 The PROPPR trial was thus, statistically speaking, a ‘negative’ trial based on the study’s primary outcomes.

The PROPPR trial was, however, designed more than a decade ago. Since its design and publication, there have been many methodological advances in clinical trial design and analysis. Two key areas revolve around the optimal timing of mortality outcomes and the increasing popularity of Bayesian analytical frameworks.

Recent data from multiple high-quality studies suggest that the most frequent causes of trauma-related deaths change over time during a patient’s resuscitation and hospital course.2–4 Early deaths are mostly related to hemorrhage, while later deaths are more consistently secondary to traumatic brain injuries and multiorgan failure.5–8 It makes biological sense that interventions which improve hemorrhage control should be studied over the time period when bleeding occurs. Recently published guidelines for trauma studies—developed at the National Heart, Lung, and Blood Institute (NHLBI) and US Department of Defense (DoD) convened consensus conference—recommend using all-cause mortality at 3–6 hours from arrival as the optimal study end point when assessing the treatment effects on mortality secondary to hemorrhagic shock.2–4 9 10

The aim of this post hoc analysis was to evaluate the effects of a balanced resuscitation strategy (1:1:1) vs a red cell heavy (1:1:2) strategy on mortality at earlier time points while using Bayesian methods. Although a detailed description of Bayesian statistics is outside the realm of this particular paper, a comprehensive comparison of Bayesian and frequentist statistical approaches structured around the PROPPR trial was recently published by our group.11 Briefly, Bayesian approaches offer an alternative statistical framework that estimates the probability of a treatment effect, as opposed to frequentist statistical methods which most often are used to dichotomize results as ‘positive’ or ‘negative’ based on traditionally selected p values.12 13 This study, therefore, sought to conduct a Bayesian analysis of the data from the PROPPR trial in order to re-evaluate the effects of a 1:1:1 resuscitation strategy versus a 1:1:2 approach on mortality at the 1, 3, 6, 12, 18, and 24 hours resuscitation time points.