LAM undertook analyses, contributed to discussions and commented critically on drafts. THMM contributed examples, contributed to discussions and commented critically on drafts. EAR and JT contributed to discussions and commented critically on drafts. DMC co-led the project, contributed to discussions and drafted extensive parts of the paper. All authors approved the final version of the manuscript. Provenance and peer review Not commissioned; externally peer reviewed. You will be able to get a quick price and instant permission to reuse the content in many different ways.
Synthesizing Qualitative and Quantitative Health Evidence: A Guide to Methods
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Log in via Institution. Email alerts. Article Text. Article menu. Synthesising quantitative evidence in systematic reviews of complex health interventions. Abstract Public health and health service interventions are typically complex: they are multifaceted, with impacts at multiple levels and on multiple stakeholders. Statistics from Altmetric. Background Public health and health service interventions are typically complex. View this table: View inline View popup. Table 1 Quantitative synthesis possibilities to address aspects of complexity.
Table 2 Quantitative graphical and synthesis approaches mentioned in the paper, with their main strengths and weaknesses in the context of complex interventions. The importance of the research question The starting point in any synthesis of quantitative evidence is a clear purpose. Considering the types of studies available Studies of the effects of complex interventions may be randomised or non-randomised, and often involve clustering of participants within social or organisational units.
Preparing for a quantitative synthesis Before undertaking a quantitative synthesis of complex interventions, it can be helpful to begin the synthesis non-quantitatively, looking at patterns and characteristics of the data identified.
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Options when effect size estimates cannot be obtained or studies are too diverse to combine Graphical approaches Graphical displays can be very valuable to illustrate patterns in results of studies. Figure 1 Example graphical displays of data from a review of interventions to promote breast feeding, for the outcome of continued breast feeding up to 23 months. Statistical methods not based on effect size estimates We have mentioned that a frequent problem is that standard meta-analysis methods cannot be used because data are not available in a similar format from every study.
Standard synthesis methods Meta-analysis for overall effect Probably the most familiar approach to meta-analysis is that of estimating a single summary effect across similar studies. Exploring heterogeneity Diversity in the types of participants and contexts , interventions and outcomes are key to understanding sources of complexity.
Synthesis methods for understanding components of the intervention When interventions comprise distinct components, it is attractive to separate out the individual effects of these components. Box 1 Example of understanding components of psychosocial interventions for coronary heart disease Welton et al reanalysed data from a Cochrane review 89 of randomised controlled trials assessing the effects of psychological interventions on mortality and morbidity reduction for people with coronary heart disease. Figure 2 Intervention components in the studies integrated by Welton et al a sample of 18 from 56 active treatment arms.
Synthesis methods for understanding mechanisms of action An alternative purpose of a synthesis is to gain insight into the mechanisms of action behind an intervention, to inform its generalisability or applicability to a particular context. Figure 3 Theoretical diabetes care model adapted from Brown et al Box 2 Example of a model-driven meta-analysis for type 2 diabetes Brown et al present a model-driven meta-analysis of correlational research on psychological and motivational predictors of diabetes outcomes, with medication and dietary adherence factors as mediators. Synthesis approaches for understanding complexities of the system Syntheses may seek to address complexities of the system to understand either the impact of the system on the effects of the intervention or the effects of the intervention on the system.
Box 3 Example of a mathematical modelling approach for soft drinks industry levy Briggs et al examined the potential impact of a soft drinks levy in the UK, considering possible different types of response to the levy by industry. Figure 4 Simplified version of the conceptual model used by Briggs et al a dapted from Briggs et al Considerations of bias and relevance It is always important to consider the extent to which 1 The findings from each study have internal validity, particularly for non-randomised studies which are typically at higher risk of bias.
Conclusion Our review of quantitative synthesis methods for evaluating the effects of complex interventions has outlined many possible approaches that might be considered by those collating evidence in support of guideline development. References 1. Implications of a complexity perspective for systematic reviews and guideline development in health decision making.
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