Cross-sectional and the empirical Bayes (EB) before–after are two of the most common methods for estimating crash modification factors (CMFs). The EB before–after method has now been accepted as one way of addressing the potential bias caused by the regression to the mean problem. However, sometimes before–after methods may not feasible because of the lack of data from before and after periods. In those cases, researchers rely on cross-sectional studies to develop CMFs. However, cross-sectional studies may provide biased CMFs through confounding. The propensity score (PS) matching method, along with cross-sectional regression models, is one of the methods that can be used to address confounding. Though PS methods are widely used in epidemiology and other studies, there are only a few studies that have used PS matching methods to estimate CMFs. The intent of this study is to evaluate and compare the performance of cross-sectional regression models using PS matching methods with the results from the EB and traditional cross-sectional methods. The comparisons were conducted using two carefully selected simulated datasets. The results indicate that optimal propensity score distance (PSD) matching with maximum variable ratio of 5 performed quite well compared with the EB before–after and the traditional cross-sectional methods.

前后的横截面和经验贝叶斯（EB）是估计碰撞修正因子（CMF）的两种最常用方法。EB之前-之后方法现在已被视为解决因回归均值问题而引起的潜在偏差的一种方法。但是，有时由于前后缺乏数据，因此前后方法可能不可行。在这些情况下，研究人员依靠横断面研究来开发CMF。但是，横断面研究可能会因混淆而提供有偏差的CMF。倾向得分（PS）匹配方法以及横截面回归模型是可用于解决混杂问题的方法之一。尽管PS方法广泛用于流行病学和其他研究中，但只有少数研究使用PS匹配方法来估计CMF。本研究的目的是评估和比较使用PS匹配方法的横截面回归模型的性能与EB和传统横截面方法的结果。使用两个精心选择的模拟数据集进行比较。结果表明，最佳可变性得分距离（PSD）与最大可变比率为5相匹配，与之前和之后的EB以及传统的横截面方法相比，效果都很好。