Hu Zonghui, Follmann Dean
Biostatistics Research Branch, National Institute of Allergy and Infectious Disease, National Institutes of Health, 6700A Rockledge Drive, MSC 7609, Bethesda, MD, USA.
Stat Med. 2007 May 30;26(12):2433-48. doi: 10.1002/sim.2720.
This paper develops methods of analysis for active extension clinical trials. Under this design, patients are randomized to treatment or placebo for a period of time (period 1), and then all patients receive treatment for an additional period of time (period 2). We assume a continuous outcome is measured at baseline and at the end of the two consecutive periods. If only period 1 data is available, classic estimators of the treatment effect include the change score, analysis of covariance, and maximum likelihood (ML). We show how to extend these estimators by incorporating period 2 data which we refer to as the period 2 estimators. Under the assumption that the mean responses for treatment and placebo arms are the same at the end of period 2, the new estimators are unbiased and more efficient than estimators that ignore period 2 data. If this assumption is not met, the period 2 tests may be more powerful than period 1 tests, but the estimators are biased downward (upward) if the treatment effect during period 2 is larger (smaller) in treatment arm than the placebo arm. In general, the proposed period 2 procedure can provide an efficient way to supplement but not supplant the usual period 1 analysis.
本文开发了用于主动扩展临床试验的分析方法。在这种设计下，患者被随机分配接受治疗或安慰剂一段时间（第1阶段），然后所有患者再接受一段时间的治疗（第2阶段）。我们假设在基线以及连续两个阶段结束时测量连续的结果。如果只有第1阶段的数据可用，治疗效果的经典估计方法包括变化分数、协方差分析和最大似然法（ML）。我们展示了如何通过纳入第2阶段的数据（我们称为第2阶段估计量）来扩展这些估计量。在第2阶段结束时治疗组和安慰剂组的平均反应相同的假设下，新的估计量是无偏的，并且比忽略第2阶段数据的估计量更有效。如果不满足这个假设，第2阶段的检验可能比第1阶段的检验更有功效，但如果治疗组在第2阶段的治疗效果比安慰剂组大（小），估计量会向下（向上）有偏差。一般来说，所提出的第2阶段程序可以提供一种有效的方法来补充但不是取代通常的第1阶段分析。
