Regression discontinuity design studies: a guide for health researchers

Everyday medical practice is largely shaped by evidence from clinical research activities, such as understanding treatment effects of prescribed drugs, changes in clinical guidelines, or program evaluation of medical services. While such evidence would ideally come from randomized controlled trials, these trials are not always feasible and are often conducted under strict criteria that might differ from real world settings; therefore, clinicians and other medical researchers often must resort to observational studies for answering key research questions. However, associational observational analysis is prone to multiple sources of bias, which leaves many medical researchers with limited options for conducting defensible causal inference research.Quasi-experimental methods, when used appropriately, can help to improve the validity of causal evidence generated from observational settings while also providing estimates potentially more applicable in real world settings than those from randomized controlled trials. One such method, regression discontinuity design (RDD), compares the outcomes of individuals who are…
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