Commentary: Can a quasi-experimental design be a better idea than an experimental one?

Epidemiology

Volumn 27, Issue 4, 2016, Pages 500-502

  Labrecque, Jeremy Alexander ^a   Kaufman, Jay Scott ^a  

^a MCGILL UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CHOLESTEROL;

ACCURACY; CARDIOVASCULAR RISK; DECISION MAKING; HEALTH CARE POLICY; HUMAN; LINEAR REGRESSION ANALYSIS; NOTE; OBESITY; OBSERVATIONAL STUDY; PRIORITY JOURNAL; PROBABILITY; PROSPECTIVE STUDY; QUASI EXPERIMENTAL STUDY; RANDOMIZATION; RANDOMIZED CONTROLLED TRIAL (TOPIC); SELECTION BIAS; SIMULATION; SYSTOLIC BLOOD PRESSURE; VALIDITY; METHODOLOGY;

RESEARCH DESIGN;

EID: 84962068237     PISSN: 10443983     EISSN: 15315487     Source Type: Journal    
DOI: 10.1097/EDE.0000000000000485     Document Type: Note

References (7)

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2. Lee DS, Lemieux T. Regression discontinuity designs in economics. J Econ Lit. 2010;48:281-355.
3. Bor J, Moscoe E, Mutevedzi P, Newell ML, Bärnighausen T. Regression discontinuity designs in epidemiology: causal inference without randomized trials. Epidemiology. 2014;25:729-737.
4. Finkelstein MO, Levin B, Robbins H. Clinical and prophylactic trials with assured new treatment for those at greater risk: II. Examples. Am J Public Health. 1996;86:696-705.
5. van Leeuwen N, Lingsma HF, de Craen T, et al. Regression discontinuity design: simulation and application in two cardiovascular trials with continuous outcomes. Epidemiology. 2016;27:503-511.
6. Bor J, Moscoe E, Bärnighausen T. Three approaches to causal inference in regression discontinuity designs. Epidemiology. 2015;26:e28-30; discussion e30.
7. Rubin DB. Assignment to Treatment Group on the Basis of a Covariate. Journal of Educational and Behavioral statistics. 1977;6:377-401.