Different ways to estimate treatment effects in randomised controlled trials.
Different ways to estimate treatment effects in randomised controlled trials.
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DOI：
10.1016/j.conctc.2018.03.008
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发表时间：
2018-06
影响因子：
1.5
通讯作者：
M H
中科院分区：
文献类型：
--
作者：
J T;L B;T H;J R;M W;M H
关于有成果国家工作队数据的分析，关于是否应对结果变量的基准值进行调整的问题正在进行辩论。当进行调整时，对于应该如何进行调整，存在着许多误解。因此，这篇教育论文的目的是：1)解释在随机对照试验中用于评估治疗效果的不同方法，2)用真实的例子说明不同的方法，3)对如何分析随机对照试验数据提出建议。对协方差纵向分析、重复测量分析(其中也使用基线值作为结果)和变化分析从理论上进行了解释，并将其应用于研究收缩压降低治疗的示例数据集。结果表明，应考虑到基线的差异，定期重复测量分析和定期变化分析没有根据两组之间的基线差异进行调整，因此导致对治疗效果的有偏见的估计。在现实生活中，由于治疗组和对照组在基线上的差异，不同的方法导致了对治疗效果的不同估计。关于随机对照试验数据的分析，建议使用纵向协方差分析或重复测量分析，不带处理变量，但在模型中考虑处理与时间之间的交互作用。
Regarding the analysis of RCT data there is a debate going on whether an adjustment for the baseline value of the outcome variable should be made. When an adjustment is made, there is a lot of misunderstanding regarding the way this should be done. Therefore, the aims of this educational paper are: 1) to explain different methods used to estimate treatment effects in RCTs, 2) to illustrate the different methods with a real life example and 3) to give an advise on how to analyse RCT data. Longitudinal analysis of covariance, repeated measures analysis in which also the baseline value is used as outcome and the analysis of changes were theoretically explained and applied to an example dataset investigating a systolic blood pressure lowering treatment. It was shown that differences at baseline should be taken into account and that regular repeated measures analysis and regular analysis of changes did not adjust for the baseline differences between the groups and therefore lead to biased estimates of the treatment effect. In the real life example, due to the differences at baseline between the treatment and control group, the different methods lead to different estimates of the treatment effect. Regarding the analysis of RCT data, it is advised to use longitudinal analysis of covariance or a repeated measures analysis without the treatment variable, but with the interaction between treatment and time in the model.