Larry V Hedges1, Jason A Saul2, Chris Cyr3, Mackenzie Magnus3, Lori A J Scott-Sheldon4,5, Deborah Young-Hyman6, Laura Kettel Khan7. 1. Department of Statistics, Northwestern University, Evanston, IL, USA. 2. Center for Impact Sciences, Harris School of Public Policy, University of Chicago, Chicago, IL, USA. 3. Impact Genome Project, Mission Measurement, Chicago, IL, USA. 4. Centers for Behavioral and Preventive Medicine, The Miriam Hospital, Providence, RI, USA. 5. Department of Psychiatry and Human Behavior, Alpert School of Medicine, Brown University, Providence, RI, USA. 6. Office of Behavioral and Social Science Research, National Institutes of Health, Bethesda, MD, USA. 7. Division of Nutrition, Physical Activity, and Obesity, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Abstract
Introduction: There is a great need for analytic techniques that allow for the synthesis of learning across seemingly idiosyncratic interventions. Objectives: The primary objective of this paper is to introduce taxonomic meta-analysis and explain how it is different from conventional meta-analysis. Results: Conventional meta-analysis has previously been used to examine the effectiveness of childhood obesity prevention interventions. However, these tend to examine narrowly defined sections of obesity prevention initiatives, and as such, do not allow the field to draw conclusions across settings, participants, or subjects. Compared with conventional meta-analysis, taxonomic meta-analysis widens the aperture of what can be examined to synthesize evidence across interventions with diverse topics, goals, research designs, and settings. A component approach is employed to examine interventions at the level of their essential features or activities to identify the concrete aspects of interventions that are used (intervention components), characteristics of the intended populations (target population or intended recipient characteristics), and facets of the environments in which they operate (contextual elements), and the relationship of these components to effect size. In addition, compared with conventional meta-analysis methods, taxonomic meta-analyses can include the results of natural experiments, policy initiatives, program implementation efforts and highly controlled experiments (as examples) regardless of the design of the report being analyzed as long as the intended outcome is the same. It also characterizes the domain of interventions that have been studied. Conclusion: Taxonomic meta-analysis can be a powerful tool for summarizing the evidence that exists and for generating hypotheses that are worthy of more rigorous testing.
Introduction: There is a great need for analytic techniques that allow for the synthesis of learning across seemingly idiosyncratic interventions. Objectives: The primary objective of this paper is to introduce taxonomic meta-analysis and explain how it is different from conventional meta-analysis. Results: Conventional meta-analysis has previously been used to examine the effectiveness of childhood obesity prevention interventions. However, these tend to examine narrowly defined sections of obesity prevention initiatives, and as such, do not allow the field to draw conclusions across settings, participants, or subjects. Compared with conventional meta-analysis, taxonomic meta-analysis widens the aperture of what can be examined to synthesize evidence across interventions with diverse topics, goals, research designs, and settings. A component approach is employed to examine interventions at the level of their essential features or activities to identify the concrete aspects of interventions that are used (intervention components), characteristics of the intended populations (target population or intended recipient characteristics), and facets of the environments in which they operate (contextual elements), and the relationship of these components to effect size. In addition, compared with conventional meta-analysis methods, taxonomic meta-analyses can include the results of natural experiments, policy initiatives, program implementation efforts and highly controlled experiments (as examples) regardless of the design of the report being analyzed as long as the intended outcome is the same. It also characterizes the domain of interventions that have been studied. Conclusion: Taxonomic meta-analysis can be a powerful tool for summarizing the evidence that exists and for generating hypotheses that are worthy of more rigorous testing.
Authors: D L Katz; M C Karlsen; M Chung; M M Shams-White; L W Green; J Fielding; A Saito; W Willett Journal: BMC Med Res Methodol Date: 2019-08-20 Impact factor: 4.615
Authors: Emily C Gathright; Elena Salmoirago-Blotcher; Julie DeCosta; Marissa L Donahue; Melissa M Feulner; Dean G Cruess; Rena R Wing; Michael P Carey; Lori A J Scott-Sheldon Journal: Health Psychol Date: 2021-09 Impact factor: 5.556