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Data censoring and parametric distribution assignment in the development of injury risk functions from biochemical data

机译：数据审查和参数分布分配在生化数据中伤害风险函数的发展中

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Biomechanical data are often assumed to be doubly censored. In this paper, this assumption is evaluated critically for several previously published sets of data. Injury risk functions are compared using simple logistic regression and using survival analysis with 1) the assumption of doubly censored data and 2) the assumption of right-censored (uninjured specimens) and uncensored (injured) data. It is shown that the injury risk functions that result from these differing assumptions are not similar and that some experiments will require a preliminary assessment of data censoring prior to finalizing the experimental design. Some types of data are obviously doubly censored (e.g., chest deflection as a predictor of rib fracture risk), but many types are not left censored since injury is a force-limiting phenomenon (e.g., axial force as a predictor of tibia fracture). Guidelines for determining the censoring for various types of experiment are presented. This paper also develops injury risk functions using parametric models having four distributions: Weibull, logistic, log-normal, and normal. The goodness of fit for each of these distributions is assessed using the adjusted Anderson-Darling statistic and by comparing the shape of the risk curve to the non-parametric Consistent-Threshold model. We show that none of the parametric distributions is consistently more appropriate than any other for the datasets considered here and that the parametric models differ appreciably only at the tails (risk below 10% or above 90%), where little data are available to rank them. Furthermore, no parametric model can be shown to be a better representation of the non-parametric model. It is concluded that most experimental programs do not collect sufficient data to justify one parametric distribution over another. It is also concluded that a non-parametric model, while the best representation of the data at hand, is not necessarily the best representation of risk for a larger population since it underestimates injury risk at the low end and overestimates risk at the high end.
机译：常常认为生物力学数据是双拷贝的。在本文中，对于几组先前发布的数据，批判性地评估该假设。使用简单的逻辑回归和使用生存分析来比较伤害风险功能，并使用生存分析与1）双重审查数据的假设和2）对右审查（未受伤标本）和未经审查（受伤）数据的假设的假设。结果表明，由这些不同的假设产生的伤害风险功能并不相似，一些实验需要在完成实验设计之前对数据审查进行初步评估。某些类型的数据显然是双击（例如，作为肋骨骨折风险的预测器的胸部偏转），但是许多类型的官方未被缩放，因为损伤是一种力限制现象（例如，作为胫骨骨折预测的轴向力）。提出了确定各种类型试验审查的指导方针。本文还使用具有四个分布的参数模型来开发伤害风险函数：Weibull，Logistic，Log-Normal和Normal。使用调整的Anderson-Darling统计来评估适用于这些分布中的每一个的优度，并通过将风险曲线的形状与非参数一致阈值模型进行比较。我们表明，在此考虑的数据集中没有任何参数分布始终如一的是，并且参数型号仅在尾部（风险低于10％或高于90％）的情况下显着差异，其中很少的数据可以对它们进行排名。此外，没有参数模型可以被证明是非参数模型的更好表示。得出结论，大多数实验程序不收集足够的数据，以证明一个参数分布在另一个上。还得出结论，非参数模型，而在手中的数据的最佳代表中，不一定是较大人群的风险的最佳表示，因为它低于低端的伤害风险，并且高估高端风险。

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