Discussion
traumatic injuries, death, PROPPR
EXSANGUINATION
Through failing to reject the null hypothesis, the PROPPR Trial was unable to demonstrate a significant difference in overall mortality rates between a 1:1:1 vs a 1:1:2 resuscitation strategy in patients with traumatic injuries at 24 hours and 30 days, the study’s original primary end points. Secondary outcomes suggested the 1:1:1 strategy demonstrated a reduction in death from exsanguination at 24 hours, which was not seen at 30 days.The PROPPR Trial’s frequentist results, which described no statistical difference in mortality rates between the tested strategies, fundamentally differ from our bayesian findings. The original frequentist claim is based on the resultant To fully explore the meaning surrounding the differing conclusions, an understanding of the fundamental concepts and limitations inherent to frequentist statistics remains imperative. The inability to reject the null hypothesis from the original PROPPR analysis resulted in a negative study. The commonly accepted dichotomous categorization (ie, negative vs positive) of a study is a direct biproduct of the study’s In addition, Bayesian approaches, although less well known, offer the ability to overcome the aforementioned challenges while providing more clinically useful and intuitive levels of detail compared with frequentist statistics. These methods are not reliant on Interestingly, bayesian techniques can evaluate the probability of specific hypotheses over various threshold ranges, creating a plethora of clinically relevant data. Within our analysis, we not only evaluated the probability that a 1:1:1 resuscitation strategy was superior to a 1:1:2 approach (ie, RR, <1.0), but were also able to evaluate the degree of superiority by assigning probability values over different RR thresholds. These values can be used clinically to help further aid with the medical decision-making process. For instance, we found an 85% probability that there was at least a 10% RR reduction in mortality at 24 hours associated with the 1:1:1 approach compared with the 1:1:2 strategy (ie, RR, <0.9). However, the probability that the reduction in mortality RR using a 1:1:1 approach was greater than 30% (ie, RR, <0.7) was only 46%. From this we can deduce that the RR associated with mortality reduction using a balanced resuscitation would likely be at most between 20% and 30% based on the associated RR crossing the 50% threshold within this range. While these RR thresholds were only assessed using a uniform prior in this analysis to limit prior influence, these bayesian RR threshold analyses allow for similar techniques to be performed that incorporate a series of prior information ranging from a highly skeptical to a highly enthusiastic in support of a hypothesis in question. By using a range of informed priors, researchers can identify the ranges of potentially possible outcomes over a series of hypothetical scenarios.Finally, bayesian approaches offer the ability to create complex multivariate models in an easily digestible fashion. For instance, while a detailed analysis remains beyond the scope of this paper, one could readily assess for correlations between studied variables to elucidate novel outcome metrics, such as the probability that balanced transfusions lead to both a lower mortality and a lower number of total blood products transfused. This level of granularity provides opportunities to deepen one’s knowledge on projected treatment effects beyond the frequentist statistical methods.