Deriving injury risk curves using survival analysis from biomechanical experiments. Issue 14 (3rd October 2016)

• Record Type: Journal Article
• Authors: Yoganandan, Narayan; Banerjee, Anjishnu; Hsu, Fang-Chi; Bass, Cameron R.; Voo, Liming; Pintar, Frank A.; Gayzik, F. Scott
• Is Part Of: Journal of biomechanics. Volume 49:Issue 14(2016)
• Page Start: 3260
• Page End: 3267
• Publication Date: 2016-10-03
• Subjects: Biomechanical experiments -- Confidence intervals -- Impact loading -- Probability curves -- Survival analysis; Animal mechanics -- Periodicals; Biomechanics -- Periodicals; Biomechanics -- Periodicals; Mécanique animale -- Périodiques; Biomécanique -- Périodiques; Electronic journals; 571.4305
• DOI: 10.1016/j.jbiomech.2016.08.002
• Languages: English
• ISSNs: 0021-9290

Abstract

Abstract: Injury risk curves from biomechanical experimental data analysis are used in automotive studies to improve crashworthiness and advance occupant safety. Metrics such as acceleration and deflection coupled with outcomes such as fractures and anatomical disruptions from impact tests are used in simple binary regression models. As an improvement, the International Standards Organization suggested a different approach. It was based on survival analysis. While probability curves for side-impact-induced thorax and abdominal injuries and frontal impact-induced foot-ankle-leg injuries are developed using this approach, deficiencies are apparent. The objective of this study is to present an improved, robust and generalizable methodology in an attempt to resolve these issues. It includes: (a) statistical identification of the most appropriate independent variable (metric) from a pool of candidate metrics, measured and or derived during experimentation and analysis processes, based on the highest area under the receiver operator curve, (b) quantitative determination of the most optimal probability distribution based on the lowest Akaike information criterion, (c) supplementing the qualitative/visual inspection method for comparing the selected distribution with a non-parametric distribution with objective measures, (d) identification of overly influential observations using different methods, and (e) estimation of confidence intervals using techniques more appropriate to the underlying survival statistical model. These clear and quantified details can be easily implemented with commercial/open source packages. They can be used in retrospective analysis and prospective design of experiments, and in applications to different loading scenarios such as underbody blast events. The feasibility of the methodology is demonstrated using post mortem human subject experiments and 24 metrics associated with thoracic/abdominal injuries in side-impacts.