On Efficiency of Constrained Longitudinal Data Analysis Versus Longitudinal Analysis of Covariance
On Efficiency of Constrained Longitudinal Data Analysis Versus Longitudinal Analysis of Covariance
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DOI：
10.1111/j.1541-0420.2009.01332.x
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发表时间：
2010-09-01
期刊：
影响因子：
1.9
通讯作者：
Lu, Kaifeng
中科院分区：
文献类型：
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作者：
Lu, Kaifeng
在随机临床试验中，通常在基线访问和随机后几个时间点收集每个受试者的测量数据。在协方差分析中，基线后的值构成反应向量，基线值被视为协变量，可用于评估基线后时间点的治疗差异。梁和泽格(2000，Sankhya：The India Journal of Statistics，Series B 62,134-148)提出了一种受约束的纵向数据分析方法，其中基线值与基线后值一起包含在响应向量中，并且由于随机化的结果，对模型施加了跨治疗组的共同基线平均值的约束。如果基线值有缺失，则约束纵向数据分析在估计基线后时间点的处理差异方面比纵向协方差分析更有效。效率增益随着未达到基线的受试者数量和缺失所有后基线值的受试者数量而增加，而对于后置设计，效率增益随着基线和后基线数值之间的绝对相关性而降低。
In randomized clinical trials, measurements are often collected on each subject at a baseline visit and several post-randomization time points. The longitudinal analysis of covariance in which the postbaseline values form the response vector and the baseline value is treated as a covariate can be used to evaluate the treatment differences at the postbaseline time points. Liang and Zeger (2000, Sankhya: The Indian Journal of Statistics, Series B 62, 134-148) propose a constrained longitudinal data analysis in which the baseline value is included in the response vector together with the postbaseline values and a constraint of a common baseline mean across treatment groups is imposed on the model as a result of randomization. If the baseline value is subject to missingness, the constrained longitudinal data analysis is shown to be more efficient for estimating the treatment differences at postbaseline time points than the longitudinal analysis of covariance. The efficiency gain increases with the number of subjects missing baseline and the number of subjects missing all postbaseline values, and, for the pre-post design, decreases with the absolute correlation between baseline and postbaseline values.