In describing the impact of an intervention, a single effect size, odds ratio, or other summary measure is often employed. This single measure is useful in calibrating the effect of one intervention against others, but it is less meaningful when the intervention displays variation in impact. A single intervention trial can show differential effects when subgroups respond differentially, when impact varies by environmental context, or when there is varying impact with different outcome measures or across follow-up time. This article presents a multilevel mixture modeling approach for meta-analyses that summarizes these sources of impact variation across trials and measured outcomes.
- Effect sizes
- Mixture modeling
- Multilevel modeling
ASJC Scopus subject areas
- Pediatrics, Perinatology, and Child Health
- Developmental and Educational Psychology
- Life-span and Life-course Studies