Human Pelvis Bayesian Injury Probability Curves From Whole Body Lateral Impact Experiments

Abstract

Injury criteria are used in military, automotive, and aviation environments to advance human safety. While injury risk curves (IRCs) for the human pelvis are published under vertical loading, there is a paucity of analysis that describe IRCs under lateral impact. The objective of the present study is to derive IRCs under this mode. Published data were used from 60 whole-body postmortem human surrogate (PMHS) tests that used repeated testing protocols. In the first analysis, from single impact tests, all injury data points were considered as left censored and noninjury points were considered as right censored, while repeated testing results were treated as interval censored data. In the second analysis, injury data were treated uncensored. Peak force was used as the response variable. Age, total body mass, gender, and body mass index (BMI) were used as covariates in different combinations. Bayesian survival analysis model was used to derive the IRCs. Plus-minus 95% credible intervals (CI) and their normalized CI sizes (NCIS) were obtained. This is the first study to develop IRCs in whole body PMHS tests to describe the human pelvic tolerance under lateral impact using Bayesian models.

Uncertainty Evaluations for Risk Assessment in Impact Injuries and Implications for Clinical Practice

“…Likelihood-based alternatives may include Bayesian methods: a prior is constructed based on past experiments and the standard parametric survival regression likelihoods are used in conjunction with the priors to yield Bayesian credible intervals (Ibrahim et al, 2001). One of the major advantages of a Bayesian credible interval is that it can be interpreted in probabilistic terms (Yoganandan et al, 2020). A 95% Bayesian credible interval would be interpreted more directly and simply as the interval such that the probability of an estimate belonging to this interval is 95%, as opposed to the repeated experiment interpretation of classical frequentist confidence intervals (Ibrahim et al, 2001).…”