Methods for evaluating causality in observational studies.

Background: In clinical medical research. causality is demonstrated by randomized 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.Methods: The methods of assessing causal inferences in observational studies are described on the basis of publications retrieved by a selective literature search.Results: 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 extemal 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.Conclusion: The approaches described here can be used to derive causal inferences from observational studies. They should only be applied after the prerequisites for their use have been carefully checked