Wang Sue-Jane, Hung H M James
Division of Biometrics II, OB/OPaSS/CDER, FDA, HFD-715, Rockville, Maryland, USA.
J Biopharm Stat. 2005;15(4):605-11. doi: 10.1081/BIP-200062280.
In analysis of covariance (ANCOVA), as a result of covariate adjustment, the estimated mean difference between the two comparative treatment groups may have a better precision than the unadjusted estimate. The extent of improvement of precision depends on the correlation between the outcome variable and the covariate selected for adjustment. Therefore, for this purpose, it is desirable to apply a proper transformation to this covariate so that the transformed covariate has a stronger correlation with the outcome variable. The best predictor from the covariate for the outcome variable is the conditional expectation of the outcome variable given the covariate. Thus, a viable strategy is using regression modeling approach to search for a statistical model to well approximate the conditional expectation based on external and/or current trial data. We propose an adaptive strategy to achieve this goal if the current data are needed to help the search.
在协方差分析（ANCOVA）中，由于协变量调整的结果，两个比较治疗组之间估计的平均差异可能比未调整的估计具有更高的精度。精度提高的程度取决于结果变量与选择用于调整的协变量之间的相关性。因此，出于这个目的，对该协变量进行适当的变换是可取的，以便变换后的协变量与结果变量具有更强的相关性。协变量对结果变量的最佳预测值是给定协变量时结果变量的条件期望。因此，一种可行的策略是使用回归建模方法来搜索一个统计模型，以便根据外部和/或当前试验数据很好地逼近条件期望。如果需要当前数据来帮助搜索，我们提出一种自适应策略来实现这一目标。
