Observational Studies: Matching or Regression?
Observational Studies: Matching or Regression?
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DOI：
10.1016/j.bbmt.2015.12.005
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发表时间：
2016-03-01
影响因子：
4.3
通讯作者：
Logan, Brent R.
中科院分区：
文献类型：
--
作者：
Brazauskas, Ruta;Logan, Brent R.
在旨在评估治疗效果或比较患者组的观察性研究中，可以使用几种方法。通常，患者的基线特征可能在组间不平衡，需要进行调整以解释这一点。它可以通过适当的回归建模或进行配对研究来完成。通常选择后者，因为它使群体看起来具有可比性。在这篇文章中，我们考虑了这两种选择在至事件发生时间研究中检测治疗效果的能力。我们的研究表明，与匹配研究相比，应用于整个队列的考克斯回归模型通常是检测治疗效果的更强大的工具。来自造血细胞移植研究的真实的数据被用作示例。(C)2016年美国血液和骨髓移植协会。
In observational studies with an aim of assessing treatment effect or comparing groups of patients, several approaches could be used. Often, baseline characteristics of patients may be imbalanced between groups, and adjustments are needed to account for this. It can be accomplished either via appropriate regression modeling or, alternatively, by conducting a matched pairs study. The latter is often chosen because it makes groups appear to be comparable. In this article we considered these 2 options in terms of their ability to detect a treatment effect in time-to-event studies. Our investigation shows that a Cox regression model applied to the entire cohort is often a more powerful tool in detecting treatment effect as compared with a matched study. Real data from a hematopoietic cell transplantation study is used as an example. (C) 2016 American Society for Blood and Marrow Transplantation.