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Regression discontinuity design studies: a guide for health researchers

Sebastián Calónico, Neal Jawadekar, Katrina Kezios, Adina Zeki Al Hazzouri

2024BMJ22 citationsDOIOpen Access PDF

Abstract

As randomized controlled trials are not always feasible, quasi-experimental methods, such as regression discontinuity design, can expand the scope of clinical investigations aimed at causal inference in observational settings. However, clinical researchers are likely to be less familiar with and have less training in quasi-experimental designs. This article focuses on implementation and provides a detailed checklist, glossary, and guided example for how to conduct an analysis of regression discontinuity design, with the aim to help clinical researchers read, conduct, and interpret regression discontinuity design and, overall, to encourage its wider adoption in clinical practice.

Topics & Concepts

Regression discontinuity designObservational studyChecklistCausal inferenceDiscontinuity (linguistics)Computer scienceRegressionResearch designClinical study designInferenceScope (computer science)Regression analysisGlossaryClinical trialManagement scienceData scienceMachine learningStatisticsMedicineArtificial intelligencePsychologyCognitive psychologyMathematicsPathologyEngineeringPhilosophyProgramming languageLinguisticsMathematical analysisAdvanced Causal Inference TechniquesHealth Systems, Economic Evaluations, Quality of LifeStatistical Methods in Clinical Trials
Regression discontinuity design studies: a guide for health researchers | Litcius