Article type
Year
Abstract
Background: Measures of effect size in systematic reviews of complex interventions, like pediatric weight management, are notoriously difficult to estimate meaningfully - in part due to the highly contextually dependent nature of multicomponent interventions. In short, complex treatment configurations are compounded by variations in method of delivery, length of treatment, setting, etc. This is complexity on top of complexity.
Objectives: To devise an analytic approach for managing the layered complexity of complex interventions and understanding how different configurations of study/treatment context characteristics could affect outcomes.
Methods: Data from 73 controlled clinical trials of pediatric weight management interventions published since 2005 were extracted into an online platform using a detailed extraction form designed to capture 30 different treatment and intervention context characteristics. Multiple components analysis was used to construct a reduced dimension adjacency matrix, which was then used to derive theoretically-informed groups of similar treatment 'mixes'. Characteristic by group Chi-square analyses provided description of the identified groups. Random-effects meta-analysis was then used to estimate treatment effect differences between groups. As heterogeneity was still high, and based on the theoretical position that configurations of context characteristics (rather than single characteristics) affect treatment results, crisp set qualitative comparative analysis (QCA) was used to identify discrete 'paths' to positive or negative weight status outcomes at six and 12 months post-treatment.
Results: The analysis identified a limited number of treatment/context configurations that were consistently associated with both positive and negative weight status outcomes (Figure 1).
Conclusions: A combination of analytic procedures may be used to manage and gain a deeper understanding of heterogeneity in context dependent multicomponent interventions. This mixed approach has great potential for use in program design by practitioners to identify conditions where particular treatment mixes are likely to lead to positive or negative outcomes.
Objectives: To devise an analytic approach for managing the layered complexity of complex interventions and understanding how different configurations of study/treatment context characteristics could affect outcomes.
Methods: Data from 73 controlled clinical trials of pediatric weight management interventions published since 2005 were extracted into an online platform using a detailed extraction form designed to capture 30 different treatment and intervention context characteristics. Multiple components analysis was used to construct a reduced dimension adjacency matrix, which was then used to derive theoretically-informed groups of similar treatment 'mixes'. Characteristic by group Chi-square analyses provided description of the identified groups. Random-effects meta-analysis was then used to estimate treatment effect differences between groups. As heterogeneity was still high, and based on the theoretical position that configurations of context characteristics (rather than single characteristics) affect treatment results, crisp set qualitative comparative analysis (QCA) was used to identify discrete 'paths' to positive or negative weight status outcomes at six and 12 months post-treatment.
Results: The analysis identified a limited number of treatment/context configurations that were consistently associated with both positive and negative weight status outcomes (Figure 1).
Conclusions: A combination of analytic procedures may be used to manage and gain a deeper understanding of heterogeneity in context dependent multicomponent interventions. This mixed approach has great potential for use in program design by practitioners to identify conditions where particular treatment mixes are likely to lead to positive or negative outcomes.