Before-after study with the empirical Bayes (EB) method is the state-of-the-art approach for estimating crash modification factors (CMFs). The EB method not only addresses the regression-to-the-mean bias, but also improves accuracy. However, the performance of the CMFs derived from the EB method has never been fully investigated. This study aims to examine the accuracy of CMFs estimated with the EB method. Artificial realistic data (ARD) and real crash data are used to evaluate the CMFs. The results indicate that: 1) The CMFs derived from the EB before-after method are nearly the same as the true values. 2) The estimated CMF standard errors do not reflect the true values. The estimation remains at the same level regardless of the pre-assumed CMF standard error. The EB before-after study is not sensitive to the variation of CMF among sites. 3) The analyses with real-world traffic and crash data with a dummy treatment indicate that the EB method tends to underestimate the standard error of the CMF. Safety researchers should recognize that the CMF variance may be biased when evaluating safety effectiveness by the EB method. It is necessary to revisit the algorithm for estimating CMF variance with the EB method.

使用经验贝叶斯（EB）方法进行的前后研究是估算碰撞修正因子（CMF）的最新方法。EB方法不仅可以解决均值回归问题，还可以提高准确性。但是，从未完全研究过从EB方法派生的CMF的性能。这项研究旨在检验使用EB方法估算的CMF的准确性。人工现实数据（ARD）和实际碰撞数据用于评估CMF。结果表明：1）EB前后方法得到的CMF与真实值几乎相同。2）估计的CMF标准误差不能反映真实值。不管预先假定的CMF标准误差如何，估算值都保持在同一水平。EB前后研究对站点之间CMF的变化不敏感。3）对真实交通和崩溃数据进行了虚拟处理的分析表明，EB方法往往会低估CMF的标准误。安全研究人员应认识到，通过EB方法评估安全有效性时，CMF方差可能会产生偏差。有必要重新审视使​​用EB方法估计CMF方差的算法。