McLeod A Ian, Vingilis E R
Department of Statistical and Actuarial Sciences, University of Western Ontario, London, Ontario, Canada N6A 5B7.
Accid Anal Prev. 2008 May;40(3):1244-8. doi: 10.1016/j.aap.2007.10.007. Epub 2007 Nov 21.
The evaluation of traffic safety interventions or other policies that can affect road safety often requires the collection of administrative time series data, such as monthly motor vehicle collision data that may be difficult and/or expensive to collect. Furthermore, since policy decisions may be based on the results found from the intervention analysis of the policy, it is important to ensure that the statistical tests have enough power, that is, that we have collected enough time series data both before and after the intervention so that a meaningful change in the series will likely be detected. In this short paper, we present a simple methodology for doing this. It is expected that the methodology presented will be useful for sample size determination in a wide variety of traffic safety intervention analysis applications. Our method is illustrated with a proposed traffic safety study that was funded by NIH.
对可能影响道路安全的交通安全干预措施或其他政策进行评估,通常需要收集行政时间序列数据,比如月度机动车碰撞数据,而这些数据可能难以收集且/或成本高昂。此外,由于政策决策可能基于对政策的干预分析得出的结果,因此确保统计检验具有足够的效力非常重要,也就是说,我们在干预前后都收集了足够的时间序列数据,以便有可能检测到序列中有意义的变化。在这篇短文中,我们提出了一种简单的方法来做到这一点。预计所提出的方法将有助于在各种交通安全干预分析应用中确定样本量。我们的方法通过一项由美国国立卫生研究院资助的拟议交通安全研究进行了说明。