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

Importance: Frequentist statistical approaches are the most common strategies for clinical trial design; however, bayesian trial design may provide a more optimal study technique for trauma-related studies. Objective: To describe the outcomes of bayesian statistical approaches using data from the Pragmatic Randomized Optimal Platelet and Plasma Ratios (PROPPR) Trial. Design, Setting, and Participants: This quality improvement study performed a post hoc bayesian analysis of the PROPPR Trial using multiple hierarchical models to assess the association of resuscitation strategy with mortality. The PROPPR Trial took place at 12 US level I trauma centers from August 2012 to December 2013. A total of 680 severely injured trauma patients who were anticipated to require large volume transfusions were included in the study. Data analysis for this quality improvement study was conducted from December 2021 and June 2022. Interventions: In the PROPPR Trial, patients were randomized to receive a balanced transfusion (equal portions of plasma, platelets, and red blood cells [1:1:1]) vs a red blood cell-heavy strategy (1:1:2) during their initial resuscitation. Main Outcomes and Measures: Primary outcomes from the PROPPR trial included 24-hour and 30-day all-cause mortality using frequentist statistical methods. Bayesian methods were used to define the posterior probabilities associated with the resuscitation strategies at each of the original primary end points. Results: Overall, 680 patients (546 [80.3%] male; median [IQR] age, 34 [24-51] years, 330 [48.5%] with penetrating injury; median [IQR] Injury Severity Score, 26 [17-41]; 591 [87.0%] with severe hemorrhage) were included in the original PROPPR Trial. Between the groups, no significant differences in mortality were originally detected at 24 hours (12.7% vs 17.0%; adjusted risk ratio [RR], 0.75 [95% CI, 0.52-1.08]; P = .12) or 30 days (22.4% vs 26.1%; adjusted RR, 0.86 [95% CI, 0.65-1.12]; P = .26). Using bayesian approaches, a 1:1:1 resuscitation was found to have a 93% (Bayes factor, 13.7; RR, 0.75 [95% credible interval, 0.45-1.11]) and 87% (Bayes factor, 6.56; RR, 0.82 [95% credible interval, 0.57-1.16]) probability of being superior to a 1:1:2 resuscitation with regards to 24-hour and 30-day mortality, respectively. Conclusions and Relevance: In this quality improvement study, a post hoc bayesian analysis of the PROPPR Trial found evidence in support of 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.