Matched Comparison Group Design Standards in Systematic Reviews of Early Childhood Interventions. (June 2017)

• Record Type: Journal Article
• Authors: Thomas, Jaime; Avellar, Sarah A.; Deke, John; Gleason, Philip
• Other Names: Supplee Lauren guest-editor.; Klerman Jacob guest-editor.
• Is Part Of: Evaluation review. Volume 41:Number 3(2017)
• Page Start: 240
• Page End: 279
• Publication Date: 2017-06
• Subjects: systematic review -- nonexperimental research methods -- early childhood -- home visiting; Evaluation research (Social action programs) -- Periodicals; 361.0072
• DOI: 10.1177/0193841X17708721
• Languages: English
• ISSNs: 0193-841X

Abstract

Background: Systematic reviews assess the quality of research on program effectiveness to help decision makers faced with many intervention options. Study quality standards specify criteria that studies must meet, including accounting for baseline differences between intervention and comparison groups. We explore two issues related to systematic review standards: covariate choice and choice of estimation method. Objective: To help systematic reviews develop/refine quality standards and support researchers in using nonexperimental designs to estimate program effects, we address two questions: (1) How well do variables that systematic reviews typically require studies to account for explain variation in key child and family outcomes? (2) What methods should studies use to account for preexisting differences between intervention and comparison groups? Methods: We examined correlations between baseline characteristics and key outcomes using Early Childhood Longitudinal Study—Birth Cohort data to address Question 1. For Question 2, we used simulations to compare two methods—matching and regression adjustment—to account for preexisting differences between intervention and comparison groups. Results: A broad range of potential baseline variables explained relatively little of the variation in child and family outcomes. This suggests the potential for bias even after accounting for these variables, highlighting the need for systematic reviews to provide appropriate cautions about interpreting the results of moderately rated, nonexperimental studies. Our simulations showed that regression adjustment can yield unbiased estimates if all relevant covariates are used, even when the model is misspecified, and preexisting differences between the intervention and the comparison groups exist.