Hsiao Chuhsing Kate, Tsai Miao-Yu, Chen Ho-Min
Division of Biostatistics, Institute of Epidemiology, National Taiwan University, Taipei 100, Taiwan.
Stat Med. 2005 Nov 15;24(21):3251-67. doi: 10.1002/sim.2211.
This paper was motivated by a double-blind randomized clinical trial of myopia intervention. In addition to the primary goal of comparing treatment effects, we are concerned with the modelling of correlation that may come from two possible sources, one among the longitudinal observations and the other between measurements taken from both eyes per subject. The data are nested repeated measurements. We suggest three models for analysis. Each one expresses the correlation differently in various covariance structures. We articulate their differences and describe the implementations in estimation using commercial statistical software. The computer output can be further utilized to perform model selection with Schwarz criterion. Simulation studies are conducted to evaluate the performance under each model. Data of the myopia intervention trial are reanalysed with these models for illustration. The results indicate that atropine is more effective in reducing the progression rate, the rates are homogeneous across subjects, and, among the suggested models, the one with independent random effects of two eyes fits best. We conclude that model selection is a crucial step before making inference with estimates; otherwise the correlation may be attributed incorrectly to a different mechanism. The same conclusion applies to other variance components as well.
本文受一项近视干预双盲随机临床试验的启发。除了比较治疗效果这一主要目标外，我们还关注可能来自两个潜在来源的相关性建模，一个是纵向观察之间的相关性，另一个是每个受试者双眼测量值之间的相关性。数据是嵌套重复测量数据。我们提出了三种分析模型。每个模型在不同的协方差结构中对相关性的表达不同。我们阐述了它们的差异，并描述了使用商业统计软件进行估计的实现方法。计算机输出结果可进一步用于基于施瓦茨准则进行模型选择。进行了模拟研究以评估每个模型下的性能。为了说明问题，我们用这些模型重新分析了近视干预试验的数据。结果表明，阿托品在降低进展率方面更有效，各受试者的进展率是均匀的，并且，在所提出的模型中，双眼具有独立随机效应的模型拟合效果最佳。我们得出结论，在根据估计值进行推断之前，模型选择是关键步骤；否则，相关性可能会被错误地归因于不同的机制。同样的结论也适用于其他方差成分。
