Department of Statistics, Virginia Tech, 406A Drillfield Drive, Blacksburg, VA, 24061, USA.
Department of Statistics, Virginia Tech, 406A Drillfield Drive, Blacksburg, VA, 24061, USA; Virginia Tech Transportation Institute, 3500 Transportation Research Driver, Blacksburg, VA, 24061, USA.
Accid Anal Prev. 2020 Aug;143:105579. doi: 10.1016/j.aap.2020.105579. Epub 2020 May 29.
INTRODUCTION/OBJECTIVE: This paper evaluates the causal effects of cellphone distraction on traffic crashes using propensity score weighting approaches and naturalistic driving study (NDS) data.
We adopt three propensity score weighting approaches to estimate the causal odds ratio (OR) of cellphone use on three different event-populations, including average treatment effect (ATE) on the whole population, average treatment effect on the treated population (ATT), and average treatment effect on the overlapping population (ATO). Three types of cellphone distractions are evaluated: overall cellphone use, talking, and visual-manual tasks. The propensity scores are estimated based on driver, roadway, and environmental characteristics. The Second Strategic Highway Research Program NDS data used in this study include 3400 participant drivers with 1047 severe crashes and 19,798 random case-cohort control driving segments.
The study reveals several highly imbalanced potential confounding factors among cellphone use groups, e.g., income, age, and time of day, which could lead to biased risk estimation. All three propensity score approaches improve the balance of the baseline characteristics. The propensity score adjusted ORs differ from unweighted ORs substantially, ranging from -44.25% to 54.88%. Specifically, the adjusted ORs for young drivers are higher than unweighted ORs and these for middle-age drivers are lower. Among different cellphone related distractions, the ORs associated with visual-manual tasks (OR range: 3.47-6.63) are uniformly higher than overall cellphone distraction and cellphone talking (OR range: 0.63-4.15). Cellphone talking increases the risk for young drivers but has no significant impact on middle-age drivers.
Propensity score approaches effectively mitigate potential confounding effect caused by imbalanced driver environmental characteristics in the observational NDS data. The ATT and ATO estimands are recommended for NDS case-cohort studies. ATT reflects the effect among exposed events, i.e. crashes or controls with cellphone exposure and ATO reflects the effect among events with similar characteristics. The study confirms the significant causal effect of overall cellphone distraction on crash risk and the heterogeneity in safety impact by age group.
引言/目的:本文采用倾向评分加权法和自然驾驶研究(NDS)数据评估手机分心对交通事故的因果影响。
我们采用三种倾向评分加权法来估计三种不同事件人群中手机使用的因果比值(OR),包括全人群平均处理效应(ATE)、处理人群平均处理效应(ATT)和重叠人群平均处理效应(ATO)。评估了三种类型的手机分心:整体手机使用、通话和视觉手动任务。基于驾驶员、道路和环境特征来估计倾向得分。本研究使用的第二个战略公路研究计划 NDS 数据包括 3400 名参与者驾驶员,其中 1047 人发生严重事故,19798 人随机案例-队列对照驾驶段。
研究揭示了手机使用组之间存在一些高度不平衡的潜在混杂因素,例如收入、年龄和一天中的时间,这可能导致风险估计偏倚。三种倾向评分方法都改善了基线特征的平衡性。倾向评分调整后的 OR 与未加权 OR 有很大差异,范围从-44.25%到 54.88%。具体来说,年轻驾驶员的调整后 OR 高于未加权 OR,而中年驾驶员的调整后 OR 则低于未加权 OR。在不同的手机相关干扰中,与视觉手动任务相关的 OR (范围:3.47-6.63)均高于整体手机干扰和手机通话(范围:0.63-4.15)。手机通话增加了年轻驾驶员的风险,但对中年驾驶员没有显著影响。
倾向评分方法有效地减轻了观察性 NDS 数据中因驾驶员环境特征不平衡而导致的潜在混杂效应。建议在 NDS 病例-队列研究中使用 ATT 和 ATO 估计量。ATT 反映了暴露事件中的效果,即有或没有手机暴露的事故或对照,而 ATO 反映了具有相似特征的事件中的效果。该研究证实了整体手机干扰对事故风险的显著因果影响以及年龄组之间安全影响的异质性。