@article{ref1,
title="Comparison of crash modification factors for engineering treatments estimated by before-after empirical Bayes and propensity score matching methods",
journal="Transportation research record",
year="2021",
author="Lan, Bo and Srinivasan, Raghavan",
volume="2675",
number="1",
pages="148-160",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="0361-1981",
doi="10.1177/0361198120953778",
url="http://dx.doi.org/10.1177/0361198120953778"
}