Quantitative Sciences Unit, Stanford University, Palo Alto, CA, USA.
BMC Med Res Methodol. 2013 Jul 10;13:89. doi: 10.1186/1471-2288-13-89.
An inverse relationship between experience and risk of injury has been observed in many occupations. Due to statistical challenges, however, it has been difficult to characterize the role of experience on the hazard of injury. In particular, because the time observed up to injury is equivalent to the amount of experience accumulated, the baseline hazard of injury becomes the main parameter of interest, excluding Cox proportional hazards models as applicable methods for consideration.
Using a data set of 81,301 hourly production workers of a global aluminum company at 207 US facilities, we compared competing parametric models for the baseline hazard to assess whether experience affected the hazard of injury at hire and after later job changes. Specific models considered included the exponential, Weibull, and two (a hypothesis-driven and a data-driven) two-piece exponential models to formally test the null hypothesis that experience does not impact the hazard of injury.
We highlighted the advantages of our comparative approach and the interpretability of our selected model: a two-piece exponential model that allowed the baseline hazard of injury to change with experience. Our findings suggested a 30% increase in the hazard in the first year after job initiation and/or change.
Piecewise exponential models may be particularly useful in modeling risk of injury as a function of experience and have the additional benefit of interpretability over other similarly flexible models.
在许多职业中,经验与受伤风险之间呈负相关关系。然而,由于统计方面的挑战,很难描述经验对受伤风险的作用。特别是,由于观察到的受伤前时间等同于积累的经验量,因此受伤的基线风险成为主要关注的参数,排除了 Cox 比例风险模型作为适用的考虑方法。
我们使用一家全球铝业公司在 207 个美国工厂的 81301 名小时生产工人的数据集,比较了用于基线风险的竞争参数模型,以评估经验是否会影响入职时和之后的工作变动后的受伤风险。考虑的具体模型包括指数、威布尔和两个(假设驱动和数据驱动)两段指数模型,以正式检验经验不影响受伤风险的零假设。
我们强调了我们的比较方法的优势和我们选择的模型的可解释性:允许受伤风险的基线随经验而变化的两段指数模型。我们的研究结果表明,在工作开始后的第一年和/或变动后,受伤的风险增加了 30%。
分段指数模型在将受伤风险作为经验的函数进行建模时可能特别有用,并且比其他类似灵活的模型具有更好的可解释性。