Comparison of Crash Modification Factors for Engineering Treatments Estimated by Before-After Empirical Bayes and Propensity Score Matching Methods

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.

Automated summary

This study evaluates and compares the performance of cross-sectional regression models using PS matching methods with results from EB and traditional cross-sectional methods. The results indicate that optimal PSD matching with maximum variable ratio of 5 performs well compared to the other methods.