Statistical Analysis
PROPPR, trauma
REGRESSION
Bayesian logistic regression hierarchical models were created to compare the mortality rates between resuscitation cohorts for each of the original study’s primary end points. Hierarchical models offer the ability to account for site-specific variations in practice patterns. Noninformative priors were incorporated into the models and represent a common approach that assumes a uniform distribution for all outcomes in efforts to minimize any influence on the results. The posterior probabilities and associated 95% credible intervals (CrIs) were calculated using the median 2.5th and 97.5th percentiles of the posterior distribution for each of the PROPPR Trial’s primary end points. Cohort differences within the bayesian hierarchical models were directly compared with the frequentist results originally described within the PROPPR Trial.Additionally, posterior probabilities over a range of mortality thresholds were calculated for each of the bayesian hierarchical models to highlight the more granular possibilities offered by this approach. Each of these probabilities was characterized by an associated Bayes factor (BF) to provide an objective assessment in the form of a likelihood ratio between the competing hypotheses. The resultant BF’s degree of evidence was reported via previously accepted methods.The PROPPR Trial’s frequentist approach compared the 2 resuscitation ratios using a 2-sided Mantel-Haenszel test. The bayesian hierarchical models incorporated in this analysis used each resuscitation strategy as a fixed-level effect and each trauma center as a group-level effect. Markov chain Monte Carlo modeling via 4 chains, 1000 iterations burn-in, and 2000 saved iterations per chain with a Bernoulli family distribution was used to develop and optimize the models. All models were created using the BRMS package in R version 4.0.3 (R Project for Statistical Computing).