scholarly journals Erratum to “Propensity score methods for road safety evaluation: Practical suggestions from a simulation study” [Accid. Anal. Prev. 158 (2021) 106200]

2021 ◽  
Vol 159 ◽  
pp. 106257
Author(s):  
Yingheng Zhang ◽  
Haojie Li ◽  
N.N. Sze ◽  
Gang Ren
2008 ◽  
Vol 17 (6) ◽  
pp. 546-555 ◽  
Author(s):  
Soko Setoguchi ◽  
Sebastian Schneeweiss ◽  
M. Alan Brookhart ◽  
Robert J. Glynn ◽  
E. Francis Cook

2001 ◽  
Vol 28 (5) ◽  
pp. 804-812 ◽  
Author(s):  
Paul de Leur ◽  
Tarek Sayed

Road safety analysis is typically undertaken using traffic collision data. However, the collision data often suffer from quality and reliability problems. These problems can inhibit the ability of road safety engineers to evaluate and analyze road safety performance. An alternate source of data that characterize the events of a traffic collision is the records that become available from an auto insurance claim. In settling an auto insurance claim, a claim adjuster must make an assessment and determination of the circumstances of the event, recording important contributing factors that led to the crash occurrence. As such, there is an opportunity to access and use the claims data in road safety engineering analysis. This paper presents the results of an initial attempt to use auto insurance claims records in road safety evaluation by developing and applying a claim prediction model. The prediction model will provide an estimate of the number of auto insurance claims that can be expected at signalized intersections in the Vancouver area of British Columbia, Canada. A discussion of the usefulness and application of the claim prediction model will be provided together with a recommendation on how the claims data could be utilized in the future.Key words: road safety improvement programs, auto insurance claims, road safety analysis, prediction models.


2018 ◽  
Vol 28 (12) ◽  
pp. 3534-3549 ◽  
Author(s):  
Arman Alam Siddique ◽  
Mireille E Schnitzer ◽  
Asma Bahamyirou ◽  
Guanbo Wang ◽  
Timothy H Holtz ◽  
...  

This paper investigates different approaches for causal estimation under multiple concurrent medications. Our parameter of interest is the marginal mean counterfactual outcome under different combinations of medications. We explore parametric and non-parametric methods to estimate the generalized propensity score. We then apply three causal estimation approaches (inverse probability of treatment weighting, propensity score adjustment, and targeted maximum likelihood estimation) to estimate the causal parameter of interest. Focusing on the estimation of the expected outcome under the most prevalent regimens, we compare the results obtained using these methods in a simulation study with four potentially concurrent medications. We perform a second simulation study in which some combinations of medications may occur rarely or not occur at all in the dataset. Finally, we apply the methods explored to contrast the probability of patient treatment success for the most prevalent regimens of antimicrobial agents for patients with multidrug-resistant pulmonary tuberculosis.


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