Lu Kaifeng
Clinical Biostatistics, Merck Research Laboratories, Rahway, New Jersey 07065, USA.
Biometrics. 2010 Sep;66(3):891-6. doi: 10.1111/j.1541-0420.2009.01332.x.
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, Sankhyā: 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.
在随机临床试验中，通常会在基线访视时以及随机化后的几个时间点对每个受试者进行测量。以基线后的值作为响应向量，将基线值作为协变量的纵向协方差分析，可用于评估基线后时间点的治疗差异。Liang和Zeger（2000年，《印度统计学杂志B辑》第62卷，134 - 148页）提出了一种受限纵向数据分析方法，其中基线值与基线后的值一起包含在响应向量中，并且由于随机化，模型对各治疗组施加了共同基线均值的约束。如果基线值存在缺失情况，与纵向协方差分析相比，受限纵向数据分析在估计基线后时间点的治疗差异方面更有效。效率增益随着基线值缺失的受试者数量以及所有基线后值均缺失的受试者数量的增加而增加，并且对于前后设计，随着基线值与基线后值之间的绝对相关性降低而降低。
