Kim Sungduk, Chen Zhen, Zhang Zhiwei, Simons-Morton Bruce G, Albert Paul S
Biostatistics and Bioinformatics Branch, Division of Epidemiology, Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Rockville, MD 20852.
J Am Stat Assoc. 2013;108(502):494-503. doi: 10.1080/01621459.2013.770702.
Although there is evidence that teenagers are at a high risk of crashes in the early months after licensure, the driving behavior of these teenagers is not well understood. The Naturalistic Teenage Driving Study (NTDS) is the first U.S. study to document continuous driving performance of newly-licensed teenagers during their first 18 months of licensure. Counts of kinematic events such as the number of rapid accelerations are available for each trip, and their incidence rates represent different aspects of driving behavior. We propose a hierarchical Poisson regression model incorporating over-dispersion, heterogeneity, and serial correlation as well as a semiparametric mean structure. Analysis of the NTDS data is carried out with a hierarchical Bayesian framework using reversible jump Markov chain Monte Carlo algorithms to accommodate the flexible mean structure. We show that driving with a passenger and night driving decrease kinematic events, while having risky friends increases these events. Further the within-subject variation in these events is comparable to the between-subject variation. This methodology will be useful for other intensively collected longitudinal count data, where event rates are low and interest focuses on estimating the mean and variance structure of the process. This article has online supplementary materials.
尽管有证据表明青少年在获得驾照后的最初几个月发生车祸的风险很高，但这些青少年的驾驶行为却并未得到充分了解。自然主义青少年驾驶研究（NTDS）是美国第一项记录新获得驾照的青少年在获得驾照后的头18个月内持续驾驶表现的研究。每次行程都有诸如快速加速次数等运动学事件的计数，其发生率代表了驾驶行为的不同方面。我们提出了一个包含过度分散、异质性和序列相关性以及半参数均值结构的分层泊松回归模型。使用可逆跳跃马尔可夫链蒙特卡罗算法，在分层贝叶斯框架下对NTDS数据进行分析，以适应灵活的均值结构。我们发现，有乘客陪同驾驶和夜间驾驶会减少运动学事件，而有行为不端的朋友则会增加这些事件。此外，这些事件在个体内部的变化与个体之间的变化相当。这种方法将对其他密集收集的纵向计数数据有用，这些数据的事件发生率较低，且关注点在于估计过程的均值和方差结构。本文有在线补充材料。
