Comparison of Empirical Bayes and Propensity Score Methods for Road Safety Evaluation: A Simulation Study

The evaluation of the effects of road safety measures on road accidents has gained continuous attention among researchers in recent years. Besides the commonly-used empirical Bayes (EB) approach, the propensity score (PS) methods have been widely employed in road safety evaluation studies. However, the conditions under which these methods can provide valid estimates of treatment effects are not well understood. The authors conduct a simulation-based comparison study to provide insight into the performance of the EB and PS methods in settings with and without violation of the key assumptions of the EB and PS methods. The models investigated include the EB, inverse probability weighting (IPW), and the doubly robust (DR) methods with different model specifications and data conditions. The results suggest that most of the methods can provide unbiased estimates of the treatment effect when the models are correctly specified, although the bias of the effect estimates increases slightly for all IPW models and most DR models with a small data sample, indicating that the propensity score methods are “data hungry”. The DR method is less affected by the omission of covariates and consistently provides unbiased estimates even in the scenarios with incorrect model specification, indicating its superiority to other two methods.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ANB20 Standing Committee on Safety Data, Analysis and Evaluation.
  • Corporate Authors:

    Transportation Research Board

    ,    
  • Authors:
    • Li, Haojie
    • Graham, Daniel J
    • Ding, Hongliang
  • Conference:
  • Date: 2019

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: References; Tables;
  • Pagination: 9p

Subject/Index Terms

Filing Info

  • Accession Number: 01698094
  • Record Type: Publication
  • Report/Paper Numbers: 19-00565
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:46AM