@misc{discovery10158003,
            year = {2022},
           title = {Recalibration as an approach to context in statistical meta-analyses},
           month = {October},
       booktitle = {2022 What Works Global Summit (WWGS)},
        abstract = {Background: Using evidence from meta-analyses for local decision-making is hampered by the lack of explicit connection between the contexts in which interventions were evaluated and the context in which the evidence is to be applied. New methods are needed to address the question, 'is there any evidence that the intervention will work differently in an area like mine?' /

Methods: We reanalyzed a meta-analysis of school-based interventions to reduce fat intake. Using observational data, effect sizes were reweighted to reflect the contextual similarity of studies to four UK Local Authorities. Studies that were more like Local Authorities contributed more towards the pooled effect size. /

Results: The original meta-analysis found that the intervention was not effective in reducing fat intake. In the recalibration analyses, because contextually relevant studies showed greater effects, the recalibrated pooled effect sizes indicated a larger effect with a narrower confidence interval in three Local Authorities, but the interpretation remained unchanged in a fourth. The findings held under both fixed effect and random effects specifications. /

Discussion: For three of the local authorities examined, we have greater confidence that school-based interventions will have an impact on reducing fat intake. Recalibration may give a contextually-nuanced estimate for a given local context.},
          author = {O'Mara-Eves, Alison and Kneale, Dylan and Candy, Bridget and Sutcliffe, Katy and Oliver, Sandy and Cain, Lizzie and Hutchinson Pascal, Niccola and Thomas, James},
             url = {https://wwgs2022.mailchimpsites.com/},
        keywords = {Statistical meta-analysis, evidence synthesis, systematic review, context, generaliability, secondary data sources}
}