Causal Mediation Analysis in Single Case Experimental Designs: Introduction to the Special Issue

• Published in: Evaluation & the health professions Vol. 45; no. 1; pp. 3 - 7
• Main Authors: Miočević, Milica, Moeyaert, Mariola, Mayer, Axel, Montoya, Amanda K.
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.03.2022; SAGE PUBLICATIONS, INC
• Subjects: Between-subjects design; Clinical outcomes; Data analysis; Exceptions; Humans; Indirect effects; Longitudinal studies; Mediation Analysis; Medical diagnosis; Medical personnel; Medical research; Models, Statistical; Predictor Variables; Quantitative analysis; Research Design; Research Personnel; Special Issue: Single Subject Causal Mediation Analysis; Variables; special issue; mediation analysis; causal inference; SCEDs; introduction article
• ISSN: 0163-2787; 1552-3918; 1552-3918
• DOI: 10.1177/01632787211073194

This special issue of Evaluation and the Health Professions is dedicated to methods for causal mediation analysis in Single Case Experimental Designs (SCEDs). Mediation analysis is used to identify intermediate variables that transmit the effect of the independent variable on the outcome. Until recently, mediation analysis was mostly confined to between-subjects designs and panel studies with few exceptions. Consequently, most of the developments in causal mediation analysis have also been restricted to such designs. In applied health research, SCEDs have been used to evaluate total effects of treatments on outcomes of interest. Providing researchers with the methods for evaluating causal indirect effects for individual participants can lead to important improvements in diagnosis, treatment, and prevention. This special issue includes articles that describe advanced quantitative methods for testing mediators in SCEDs, propose and test approaches that allow for relaxing statistical assumptions that may not hold in real data, and illustrate mediation analysis for a single participant in real and simulated SCEDs data.