Yang Hanyu, Li Runze, Zucker Robert A, Buu Anne
Pennsylvania State University, University Park, USA.
University of Michigan, Ann Arbor, USA.
J R Stat Soc Ser C Appl Stat. 2016 Apr;65(3):431-444. doi: 10.1111/rssc.12123. Epub 2015 Oct 26.
This study proposes a two-stage approach to characterize individual developmental trajectories of health risk behaviors and delineate their time-varying effects on short-term or long-term health outcomes. Our model can accommodate longitudinal covariates with zero-inflated counts and discrete outcomes. The longitudinal data of a well-known study of youth at high risk for substance abuse are presented as a motivating example to demonstrate the effectiveness of the model in delineating critical developmental periods of prevention and intervention. Our simulation study shows that the performance of the proposed model improves as the sample size or number of time points increases. When there are excess zeros in the data, the regular Poisson model cannot estimate either the longitudinal covariate process or its time-varying effect well. This result, therefore, emphasizes the important role that the proposed model plays in handling zero-inflation in the data.
本研究提出了一种两阶段方法，用于刻画健康风险行为的个体发展轨迹，并描述其对短期或长期健康结果的时变影响。我们的模型可以处理零膨胀计数的纵向协变量和离散结果。一项关于药物滥用高风险青年的著名研究的纵向数据作为一个激励性例子呈现，以证明该模型在描绘预防和干预的关键发展时期方面的有效性。我们的模拟研究表明，随着样本量或时间点数量的增加，所提出模型的性能会提高。当数据中存在过多零值时，常规泊松模型无法很好地估计纵向协变量过程或其随时间变化的影响。因此，这一结果强调了所提出模型在处理数据中的零膨胀问题方面所起的重要作用。
