Signalized intersection management is a common measure of risky driving in simulator studies. In a recent randomized trial, investigators were interested in whether teenage males exposed to a risk-accepting passenger took more intersection risks in a driving simulator compared with those exposed to a risk-averse peer passenger. Analyses in this trial are complicated by the longitudinal or repeated measures that are semi-continuous with clumping at zero. Specifically, the dependent variable in a randomized trial looking at the effect of risk-accepting versus risk-averse peer passengers on teenage simulator driving is comprised of two components. The discrete component measures whether the teen driver stops for a yellow light, and the continuous component measures the time the teen driver, who does not stop, spends in the intersection during a red light. To convey both components of this measure, we apply a two-part regression with correlated random effects model (CREM), consisting of a logistic regression to model whether the driver stops for a yellow light and a linear regression to model the time spent in the intersection during a red light. These two components are related through the correlation of their random effects. Using this novel analysis, we found that those exposed to a risk-averse passenger have a higher proportion of stopping at yellow lights and a longer mean time in the intersection during a red light when they did not stop at the light compared to those exposed to a risk-accepting passenger, consistent with the study hypotheses and previous analyses. Examining the statistical properties of the CREM approach through simulations, we found that in most situations, the CREM achieves greater power than competing approaches. We also examined whether the treatment effect changes across the length of the drive and provided a sample size recommendation for detecting such phenomenon in subsequent trials. Our findings suggest that CREM provides an efficient method for analyzing the complex longitudinal data encountered in driving simulation studies.

信号交叉口管理是模拟器研究中常见的危险驾驶措施。在最近的一项随机试验中，研究人员对暴露于可接受风险的乘客中的青少年男性在驾驶模拟器中是否比暴露于厌恶风险的同伴乘客中的交叉风险更高感兴趣。该试验中的分析由于纵向或重复测量而变得复杂，这些测量是半连续的且成簇为零。具体而言，一项随机试验中的因变量研究了风险接受与风险厌恶的同伴乘客对青少年模拟器驾驶的影响，该变量由两个部分组成。离散分量测量青少年驾驶员是否停下黄灯，而连续分量测量未停止的青少年驾驶员的时间，在红灯期间在十字路口停留。为了传达此指标的两个组成部分，我们将两部分回归与相关随机效应模型（CREM）结合使用，包括对数逻辑回归模型来模拟驾驶员是否停下黄灯，而线性回归模型来模拟驾驶员花费在黄灯上的时间。红灯期间的交叉路口。这两个组成部分通过其随机效应的相关性相互关联。通过这种新颖的分析，我们发现，与那些厌恶高风险的乘客相比，那些不愿冒险的乘客在黄灯处停车的比例更高，而在红灯期间他们不停在红灯处的十字路口的平均时间更长。与研究假设和以前的分析一致的接受风险的乘客。通过模拟检查CREM方法的统计特性，我们发现，在大多数情况下，CREM比竞争方法具有更大的功能。我们还检查了治疗效果是否在驱动器的整个长度上都发生了变化，并提供了建议的样本量，以在随后的试验中检测这种现象。我们的发现表明，CREM提供了一种有效的方法来分析驾驶模拟研究中遇到的复杂纵向数据。