Institute for Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg University of Mainz; Institute of Clinical Physiology of the Italian National Research Council, Lecce, Italy; Technical University Dresden, University Hospital Carl Gustav Carus, Medical Clinic 1, Dresden; Department of Pediatric Surgery, Faculty of Medicine, Johannes Gutenberg University of Mainz; Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg; Chair of Genetic Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-Universität, München.
Dtsch Arztebl Int. 2020 Feb 14;116(7):101-107. doi: 10.3238/arztebl.2020.0101.
In clinical medical research, causality is demonstrated b controlled trials (RCTs). Often, however, an RCT cannot be conducted for ethical reasons, and sometimes for practical reasons as well. In such cases, knowledge can be derived from an observational study instead. In this article, we present two methods that have not been widely used in medical research to date.
The methods of assessing causal inferences in observational studies are described on the basis of publications retrieved by a selective literature search.
Two relatively new approaches-regression-discontinuity methods and interrupted time series-can be used to demonstrate a causal relationship under certain circumstances. The regression-discontinuity design is a quasi-experimental approach that can be applied if a continuous assignment variable is used with a threshold value. Patients are assigned to different treatment schemes on the basis of the threshold value. For assignment variables that are subject to random measurement error, it is assumed that, in a small interval around a threshold value, e.g., cholesterol values of 160 mg/dL, subjects are assigned essentially at random to one of two treatment groups. If patients with a value above the threshold are given a certain treatment, those with values below the threshold can serve as control group. Interrupted time series are a special type of regression-discontinuity design in which time is the assignment variable, and the threshold is a cutoff point. This is often an external event, such as the imposition of a smoking ban. A before-and-after comparison can be used to determine the effect of the intervention (e.g., the smoking ban) on health parameters such as the frequency of cardiovascular disease.
The approaches described here can be used to derive causal inferences ies. They should only be applied after the prerequisites for their use have been carefully checked.
在临床医学研究中，因果关系是通过对照试验（RCT）来证明的。然而，由于伦理原因，有时也由于实际原因，无法进行 RCT。在这种情况下，可以从观察性研究中获得知识。本文介绍了两种迄今为止在医学研究中尚未广泛使用的方法。
根据选择性文献检索中检索到的出版物，描述了评估观察性研究中因果关系的方法。
两种相对较新的方法——回归间断法和中断时间序列法——可以在某些情况下用于证明因果关系。回归间断设计是一种准实验方法，如果使用连续分配变量和阈值，则可以应用该方法。根据阈值将患者分配到不同的治疗方案中。对于受到随机测量误差影响的分配变量，假设在阈值周围的小间隔内，例如胆固醇值为 160mg/dL，患者基本上是随机分配到两个治疗组之一。如果将高于阈值的患者给予某种治疗，则低于阈值的患者可以作为对照组。中断时间序列是一种特殊类型的回归间断设计，其中时间是分配变量，而阈值是截止点。这通常是一个外部事件，例如实施禁烟令。可以进行前后比较来确定干预（例如禁烟令）对心血管疾病等健康参数的影响。
这里描述的方法可用于得出因果推论。只有在仔细检查了其使用前提后，才能应用这些方法。
