Yang Songshan, Cranford James A, Li Runze, Zucker Robert A, Buu Anne
1 Department of Statistics, Pennsylvania State University, University Park, PA, USA.
2 Department of Psychiatry & Addiction Research Center, University of Michigan, Ann Arbor, MI, USA.
Stat Methods Med Res. 2017 Dec;26(6):2812-2820. doi: 10.1177/0962280215610608. Epub 2015 Oct 16.
This study proposes a time-varying effect model that can be used to characterize gender-specific trajectories of health behaviors and conduct hypothesis testing for gender differences. The motivating examples demonstrate that the proposed model is applicable to not only multi-wave longitudinal studies but also short-term studies that involve intensive data collection. The simulation study shows that the accuracy of estimation of trajectory functions improves as the sample size and the number of time points increase. In terms of the performance of the hypothesis testing, the type I error rates are close to their corresponding significance levels under all combinations of sample size and number of time points. Furthermore, the power increases as the alternative hypothesis deviates more from the null hypothesis, and the rate of this increasing trend is higher when the sample size and the number of time points are larger.
本研究提出了一种时变效应模型，该模型可用于刻画健康行为的性别特异性轨迹，并对性别差异进行假设检验。相关的示例表明，所提出的模型不仅适用于多波纵向研究，也适用于涉及密集数据收集的短期研究。模拟研究表明，随着样本量和时间点数的增加，轨迹函数的估计精度会提高。在假设检验的性能方面，在样本量和时间点数的所有组合下，I型错误率都接近其相应的显著性水平。此外，功效随着备择假设与原假设的偏离程度增大而增加，当样本量和时间点数较大时，这种增加趋势的速率更高。
