Estimating the safety impacts in before–after studies using the Naïve Adjustment Method

Pei Fen Kuo, Dominique Lord

研究成果: Article

4 引文 (Scopus)

摘要

The before–after study is the most popular approach for estimating the safety impacts of an intervention or treatment. Recent research, however, has shown that the most common before–after approaches can still provide a biased estimate when an entry criterion is used and when the characteristics of the treatment and control groups are dissimilar. Recently, a new simple method, referred to as the Naïve Adjustment Method (NAM), has been proposed to mitigate the limitations identified above. Unfortunately, the effectiveness of the NAM using ‘real’ data has not yet been properly investigated. Hence, this paper examined the accuracy of the NAM when the treatment group contains sites that have different mean values. Simulated and two observed datasets were used. The results show that the NAM outperforms the Naïve, the Control Group, and the empirical Bayesian methods. Furthermore, it can be used as a simpler alternative for adjusting the Naïve estimators documented in previous studies.

原文English
頁(從 - 到)915-931
頁數17
期刊Transportmetrica A: Transport Science
13
發行號10
DOIs
出版狀態Published - 2017 十一月 26

指紋

Group

All Science Journal Classification (ASJC) codes

  • Transportation
  • Engineering(all)

引用此文

@article{194f6fdebbef4cba827e0362a5983d1b,
title = "Estimating the safety impacts in before–after studies using the Na{\"i}ve Adjustment Method",
abstract = "The before–after study is the most popular approach for estimating the safety impacts of an intervention or treatment. Recent research, however, has shown that the most common before–after approaches can still provide a biased estimate when an entry criterion is used and when the characteristics of the treatment and control groups are dissimilar. Recently, a new simple method, referred to as the Na{\"i}ve Adjustment Method (NAM), has been proposed to mitigate the limitations identified above. Unfortunately, the effectiveness of the NAM using ‘real’ data has not yet been properly investigated. Hence, this paper examined the accuracy of the NAM when the treatment group contains sites that have different mean values. Simulated and two observed datasets were used. The results show that the NAM outperforms the Na{\"i}ve, the Control Group, and the empirical Bayesian methods. Furthermore, it can be used as a simpler alternative for adjusting the Na{\"i}ve estimators documented in previous studies.",
author = "Kuo, {Pei Fen} and Dominique Lord",
year = "2017",
month = "11",
day = "26",
doi = "10.1080/23249935.2017.1352627",
language = "English",
volume = "13",
pages = "915--931",
journal = "Transportmetrica A: Transport Science",
issn = "2324-9935",
publisher = "Taylor and Francis",
number = "10",

}

TY - JOUR

T1 - Estimating the safety impacts in before–after studies using the Naïve Adjustment Method

AU - Kuo, Pei Fen

AU - Lord, Dominique

PY - 2017/11/26

Y1 - 2017/11/26

N2 - The before–after study is the most popular approach for estimating the safety impacts of an intervention or treatment. Recent research, however, has shown that the most common before–after approaches can still provide a biased estimate when an entry criterion is used and when the characteristics of the treatment and control groups are dissimilar. Recently, a new simple method, referred to as the Naïve Adjustment Method (NAM), has been proposed to mitigate the limitations identified above. Unfortunately, the effectiveness of the NAM using ‘real’ data has not yet been properly investigated. Hence, this paper examined the accuracy of the NAM when the treatment group contains sites that have different mean values. Simulated and two observed datasets were used. The results show that the NAM outperforms the Naïve, the Control Group, and the empirical Bayesian methods. Furthermore, it can be used as a simpler alternative for adjusting the Naïve estimators documented in previous studies.

AB - The before–after study is the most popular approach for estimating the safety impacts of an intervention or treatment. Recent research, however, has shown that the most common before–after approaches can still provide a biased estimate when an entry criterion is used and when the characteristics of the treatment and control groups are dissimilar. Recently, a new simple method, referred to as the Naïve Adjustment Method (NAM), has been proposed to mitigate the limitations identified above. Unfortunately, the effectiveness of the NAM using ‘real’ data has not yet been properly investigated. Hence, this paper examined the accuracy of the NAM when the treatment group contains sites that have different mean values. Simulated and two observed datasets were used. The results show that the NAM outperforms the Naïve, the Control Group, and the empirical Bayesian methods. Furthermore, it can be used as a simpler alternative for adjusting the Naïve estimators documented in previous studies.

UR - http://www.scopus.com/inward/record.url?scp=85026353574&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85026353574&partnerID=8YFLogxK

U2 - 10.1080/23249935.2017.1352627

DO - 10.1080/23249935.2017.1352627

M3 - Article

AN - SCOPUS:85026353574

VL - 13

SP - 915

EP - 931

JO - Transportmetrica A: Transport Science

JF - Transportmetrica A: Transport Science

SN - 2324-9935

IS - 10

ER -