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论治疗研究中的因果关系:观察性研究、随机实验和工具变量分析。

On causation in therapeutic research: observational studies, randomised experiments and instrumental variable analysis.

机构信息

Leiden University Medical Centre, Departments of Clinical Epidemiology, The Netherlands.

出版信息

Prev Med. 2011 Oct;53(4-5):239-41. doi: 10.1016/j.ypmed.2011.08.003. Epub 2011 Aug 12.

Abstract

The counterfactual theory defines a conceptual framework for judging causation of therapeutic interventions. The main assumption underlying the claim that a counterfactual statement holds is that compared groups are exchangeable. In observational studies for therapeutic effects causal claims are hard to justify because the lack of exchangeability between compared groups. Randomised studies have a rather straightforward causal interpretation, based on the randomisation procedure that leads to expected exchangeability between compared groups. The use of instrumental variables can theoretically overcome the problems adherent to observational data, at least to a certain extent. The general idea of instrumental variables is to mimic a randomised trial, by searching for a variable that determines the probability of exposure (treatment) but that is not in other ways associated with the outcome under study. The assumptions for causal claims in IV analyses are strong, but the alternative of avoiding causal statements based on observational data, is less appealing.

摘要

反事实理论为判断治疗干预措施的因果关系提供了一个概念框架。该理论的主要假设是,反事实陈述成立的前提是比较组是可交换的。在观察性研究中,由于比较组之间缺乏可交换性,因此很难证明治疗效果的因果关系。随机研究基于随机分组过程,得出比较组之间可预期的可交换性,因此具有较为直接的因果关系解释。在理论上,工具变量的使用可以在一定程度上克服观察性数据中存在的问题。工具变量的一般思路是通过寻找一个能够确定暴露(治疗)概率但与研究结果没有其他关联的变量,来模拟随机试验,从而克服问题。在 IV 分析中,因果关系的假设是很强的,但基于观察性数据避免因果陈述的替代方案则不那么有吸引力。

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