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      The effect direction plot revisited: Application of the 2019 Cochrane Handbook guidance on alternative synthesis methods

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          Abstract

          Effect direction (evidence to indicate improvement, deterioration, or no change in an outcome) can be used as a standardized metric which enables the synthesis of diverse effect measures in systematic reviews. The effect direction (ED) plot was developed to support the synthesis and visualization of effect direction data. Methods for the ED plot require updating in light of new Cochrane guidance on alternative synthesis methods. To update the ED plot, statistical significance was removed from the algorithm for within‐study synthesis and use of a sign test was considered to examine whether patterns of ED across studies could be due to chance alone. The revised methods were applied to an existing Cochrane review of the health impacts of housing improvements. The revised ED plot provides a method of data visualization in synthesis without meta‐analysis that incorporates information about study characteristics and study quality, using ED as a common metric, without relying on statistical significance to combine outcomes of single studies. The results of sign tests, when appropriate, suggest caution in over‐interpreting apparent patterns in effect direction, especially when the number of included studies is small. The revised ED plot meets the need for alternative methods of synthesis and data visualization when meta‐analysis is not possible, enabling a transparent link between the data and conclusions of a systematic review. ED plots may be particularly useful in reviews that incorporate nonrandomized studies, complex systems approaches, and diverse sources of evidence, due to the variety of study designs and outcomes in such reviews.

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          Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline

          In systematic reviews that lack data amenable to meta-analysis, alternative synthesis methods are commonly used, but these methods are rarely reported. This lack of transparency in the methods can cast doubt on the validity of the review findings. The Synthesis Without Meta-analysis (SWiM) guideline has been developed to guide clear reporting in reviews of interventions in which alternative synthesis methods to meta-analysis of effect estimates are used. This article describes the development of the SWiM guideline for the synthesis of quantitative data of intervention effects and presents the nine SWiM reporting items with accompanying explanations and examples.
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            Sifting the evidence-what's wrong with significance tests?

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              The harvest plot: A method for synthesising evidence about the differential effects of interventions

              Background One attraction of meta-analysis is the forest plot, a compact overview of the essential data included in a systematic review and the overall 'result'. However, meta-analysis is not always suitable for synthesising evidence about the effects of interventions which may influence the wider determinants of health. As part of a systematic review of the effects of population-level tobacco control interventions on social inequalities in smoking, we designed a novel approach to synthesis intended to bring aspects of the graphical directness of a forest plot to bear on the problem of synthesising evidence from a complex and diverse group of studies. Methods We coded the included studies (n = 85) on two methodological dimensions (suitability of study design and quality of execution) and extracted data on effects stratified by up to six different dimensions of inequality (income, occupation, education, gender, race or ethnicity, and age), distinguishing between 'hard' (behavioural) and 'intermediate' (process or attitudinal) outcomes. Adopting a hypothesis-testing approach, we then assessed which of three competing hypotheses (positive social gradient, negative social gradient, or no gradient) was best supported by each study for each dimension of inequality. Results We plotted the results on a matrix ('harvest plot') for each category of intervention, weighting studies by the methodological criteria and distributing them between the competing hypotheses. These matrices formed part of the analytical process and helped to encapsulate the output, for example by drawing attention to the finding that increasing the price of tobacco products may be more effective in discouraging smoking among people with lower incomes and in lower occupational groups. Conclusion The harvest plot is a novel and useful method for synthesising evidence about the differential effects of population-level interventions. It contributes to the challenge of making best use of all available evidence by incorporating all relevant data. The visual display assists both the process of synthesis and the assimilation of the findings. The method is suitable for adaptation to a variety of questions in evidence synthesis and may be particularly useful for systematic reviews addressing the broader type of research question which may be most relevant to policymakers.
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                Author and article information

                Contributors
                michele.hiltonboon@glasgow.ac.uk
                Journal
                Res Synth Methods
                Res Synth Methods
                10.1002/(ISSN)1759-2887
                JRSM
                Research Synthesis Methods
                John Wiley and Sons Inc. (Hoboken )
                1759-2879
                1759-2887
                05 October 2020
                January 2021
                : 12
                : 1 , Data Visualization for Evidence Synthesis ( doiID: 10.1002/jrsm.v12.1 )
                : 29-33
                Affiliations
                [ 1 ] MRC/CSO Social and Public Health Sciences Unit University of Glasgow Glasgow UK
                Author notes
                [*] [* ] Correspondence

                Michele Hilton Boon, MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, 200 Renfield Street, Glasgow G2 3AX, UK.

                Email: michele.hiltonboon@ 123456glasgow.ac.uk

                Author information
                https://orcid.org/0000-0002-2240-7923
                Article
                JRSM1458
                10.1002/jrsm.1458
                7821279
                32979023
                50a95f5e-dc0d-4fe4-a6ae-31ff5f61bf90
                © 2020 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 07 February 2020
                : 01 August 2020
                : 23 September 2020
                Page count
                Figures: 1, Tables: 0, Pages: 5, Words: 2757
                Funding
                Funded by: Medical Research Council , open-funder-registry 10.13039/501100007155;
                Award ID: MC_UU_12017/15
                Funded by: Scottish Government Chief Scientist Office
                Award ID: SPHSU15
                Categories
                Special Issue Paper
                Special Issue Papers
                Custom metadata
                2.0
                January 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.6 mode:remove_FC converted:22.01.2021

                data visualization,effect direction,standardized metric,synthesis,vote counting

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