Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Evid Based Ment Health. 2018 Feb;21(1):34-38. doi: 10.1136/eb-2017-102859. Epub 2017 Dec 29.
Results from well-conducted randomised controlled studies should ideally inform on the comparative merits of treatment choices for a health condition. In the absence of this, one attempts to use evidence from the impact of treatment when administered according to decisions of the physicians and the patients (observational evidence). Naïve comparisons between treatment options using observational evidence will lead to biased results. Under certain conditions, however, it is possible to obtain valid estimates of the comparative merits of different treatments from observational data. Causal inference can be conceptualised as a framework aiming to provide valid information about causal effects of treatments using observational evidence. It can be viewed as a missing data problem in which each patient has two outcomes: the observed outcome under the treatment actually received and a counterfactual (unobserved) outcome the patient received a different treatment. Methodological developments over the last decades clarified the appropriate conditions and methods to obtain valid comparisons. This article provides an introduction to some of these methods.
来自精心设计的随机对照研究的结果应该能够为特定健康状况的治疗选择提供比较优势的信息。在缺乏这种信息的情况下，人们试图利用根据医生和患者的决策进行治疗的影响的证据（观察性证据）。使用观察性证据对治疗方案进行简单的比较会导致有偏的结果。然而，在某些条件下，从观察性数据中获得不同治疗方法的比较优势的有效估计是可能的。因果推断可以被概念化为一个框架，旨在使用观察性证据提供关于治疗效果的有效信息。它可以被视为一个缺失数据问题，其中每个患者有两个结果：实际接受的治疗下的观察结果和一个反事实（未观察到的）结果，即患者接受了不同的治疗。过去几十年中的方法学发展阐明了获得有效比较的适当条件和方法。本文介绍了其中的一些方法。
