Change from baseline and analysis of covariance revisited
Change from baseline and analysis of covariance revisited
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DOI：
10.1002/sim.2682
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发表时间：
2006-12-30
影响因子：
2
通讯作者：
Senn, Stephen
中科院分区：
文献类型：
--
作者：
Senn, Stephen
人们常常倾向于协方差分析（ANCOVA）而不是简单的变化分数分析（SACS）。尽管如此，仍有人声称，如果两组在基线时不相等，协方差分析是有偏差的。如果所要求的平等只是在预期中，这将允许在随机临床试验中使用ANCOVA，但不允许在观察性研究中使用。这个讨论与洛德悖论有关。然而，在本说明中，它表明，对于ANCOVA提供治疗效果的无偏估计，在基线上，甚至在期望上，这不是组相等的必要条件。它还表明，虽然可以设想许多情况下ANCOVA是有偏差的，但很难想象SACS在什么情况下是无偏的，并且可以作出因果解释。版权所有(c) 2006约翰威利父子有限公司
The case for preferring analysis of covariance (ANCOVA) to the simple analysis of change scores (SACS) has often been made. Nevertheless, claims continue to be made that analysis of covariance is biased if the groups are not equal at baseline. If the required equality were in expectation only, this would permit the use of ANCOVA in randomized clinical trials but not in observational studies. The discussion is related to Lord's paradox. In this note, it is shown, however that it is not a necessary condition for groups to be equal at baseline, not even in expectation, for ANCOVA to provide unbiased estimates of treatment effects. It is also shown that although many situations can be envisaged where ANCOVA is biased it is very difficult to imagine circumstances under which SACS would then be unbiased and a causal interpretation could be made. Copyright (c) 2006 John Wiley & Sons, Ltd.