Data visualisation approaches for component network meta-analysis: visualising the data structure
Abstract Background Health and social care interventions are often complex and can be decomposed into multiple components. Multicomponent interventions are often evaluated in randomised controlled trials. Across trials, interventions often have components in common which are given alongside other co...
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| Format: | Article |
| Language: | English |
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BMC
2023-09-01
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| Series: | BMC Medical Research Methodology |
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| Online Access: | https://doi.org/10.1186/s12874-023-02026-z |
| _version_ | 1797558858387816448 |
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| author | Suzanne C. Freeman Elnaz Saeedi José M. Ordóñez-Mena Clareece R. Nevill Jamie Hartmann-Boyce Deborah M. Caldwell Nicky J. Welton Nicola J. Cooper Alex J. Sutton |
| author_facet | Suzanne C. Freeman Elnaz Saeedi José M. Ordóñez-Mena Clareece R. Nevill Jamie Hartmann-Boyce Deborah M. Caldwell Nicky J. Welton Nicola J. Cooper Alex J. Sutton |
| author_sort | Suzanne C. Freeman |
| collection | DOAJ |
| description | Abstract Background Health and social care interventions are often complex and can be decomposed into multiple components. Multicomponent interventions are often evaluated in randomised controlled trials. Across trials, interventions often have components in common which are given alongside other components which differ across trials. Multicomponent interventions can be synthesised using component NMA (CNMA). CNMA is limited by the structure of the available evidence, but it is not always straightforward to visualise such complex evidence networks. The aim of this paper is to develop tools to visualise the structure of complex evidence networks to support CNMA. Methods We performed a citation review of two key CNMA methods papers to identify existing published CNMA analyses and reviewed how they graphically represent intervention complexity and comparisons across trials. Building on identified shortcomings of existing visualisation approaches, we propose three approaches to standardise visualising the data structure and/or availability of data: CNMA-UpSet plot, CNMA heat map, CNMA-circle plot. We use a motivating example to illustrate these plots. Results We identified 34 articles reporting CNMAs. A network diagram was the most common plot type used to visualise the data structure for CNMA (26/34 papers), but was unable to express the complex data structures and large number of components and potential combinations of components associated with CNMA. Therefore, we focused visualisation development around representing the data structure of a CNMA more completely. The CNMA-UpSet plot presents arm-level data and is suitable for networks with large numbers of components or combinations of components. Heat maps can be utilised to inform decisions about which pairwise interactions to consider for inclusion in a CNMA model. The CNMA-circle plot visualises the combinations of components which differ between trial arms and offers flexibility in presenting additional information such as the number of patients experiencing the outcome of interest in each arm. Conclusions As CNMA becomes more widely used for the evaluation of multicomponent interventions, the novel CNMA-specific visualisations presented in this paper, which improve on the limitations of existing visualisations, will be important to aid understanding of the complex data structure and facilitate interpretation of the CNMA results. |
| first_indexed | 2024-03-10T17:36:20Z |
| format | Article |
| id | doaj.art-35f1c7c164674cb29e79d5bb02e7d6a8 |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-03-10T17:36:20Z |
| publishDate | 2023-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-35f1c7c164674cb29e79d5bb02e7d6a82023-11-20T09:49:30ZengBMCBMC Medical Research Methodology1471-22882023-09-0123111510.1186/s12874-023-02026-zData visualisation approaches for component network meta-analysis: visualising the data structureSuzanne C. Freeman0Elnaz Saeedi1José M. Ordóñez-Mena2Clareece R. Nevill3Jamie Hartmann-Boyce4Deborah M. Caldwell5Nicky J. Welton6Nicola J. Cooper7Alex J. Sutton8Biostatistics Research Group, Department of Population Health Sciences, University of LeicesterBiostatistics Research Group, Department of Population Health Sciences, University of LeicesterNuffield Department of Primary Care Health Sciences, University of OxfordBiostatistics Research Group, Department of Population Health Sciences, University of LeicesterNuffield Department of Primary Care Health Sciences, University of OxfordPopulation Health Sciences, Bristol Medical School, University of BristolPopulation Health Sciences, Bristol Medical School, University of BristolBiostatistics Research Group, Department of Population Health Sciences, University of LeicesterBiostatistics Research Group, Department of Population Health Sciences, University of LeicesterAbstract Background Health and social care interventions are often complex and can be decomposed into multiple components. Multicomponent interventions are often evaluated in randomised controlled trials. Across trials, interventions often have components in common which are given alongside other components which differ across trials. Multicomponent interventions can be synthesised using component NMA (CNMA). CNMA is limited by the structure of the available evidence, but it is not always straightforward to visualise such complex evidence networks. The aim of this paper is to develop tools to visualise the structure of complex evidence networks to support CNMA. Methods We performed a citation review of two key CNMA methods papers to identify existing published CNMA analyses and reviewed how they graphically represent intervention complexity and comparisons across trials. Building on identified shortcomings of existing visualisation approaches, we propose three approaches to standardise visualising the data structure and/or availability of data: CNMA-UpSet plot, CNMA heat map, CNMA-circle plot. We use a motivating example to illustrate these plots. Results We identified 34 articles reporting CNMAs. A network diagram was the most common plot type used to visualise the data structure for CNMA (26/34 papers), but was unable to express the complex data structures and large number of components and potential combinations of components associated with CNMA. Therefore, we focused visualisation development around representing the data structure of a CNMA more completely. The CNMA-UpSet plot presents arm-level data and is suitable for networks with large numbers of components or combinations of components. Heat maps can be utilised to inform decisions about which pairwise interactions to consider for inclusion in a CNMA model. The CNMA-circle plot visualises the combinations of components which differ between trial arms and offers flexibility in presenting additional information such as the number of patients experiencing the outcome of interest in each arm. Conclusions As CNMA becomes more widely used for the evaluation of multicomponent interventions, the novel CNMA-specific visualisations presented in this paper, which improve on the limitations of existing visualisations, will be important to aid understanding of the complex data structure and facilitate interpretation of the CNMA results.https://doi.org/10.1186/s12874-023-02026-zComponent network meta-analysisData visualisationMeta-analysisPresentational toolsGraphical displaysMulticomponent interventions |
| spellingShingle | Suzanne C. Freeman Elnaz Saeedi José M. Ordóñez-Mena Clareece R. Nevill Jamie Hartmann-Boyce Deborah M. Caldwell Nicky J. Welton Nicola J. Cooper Alex J. Sutton Data visualisation approaches for component network meta-analysis: visualising the data structure BMC Medical Research Methodology Component network meta-analysis Data visualisation Meta-analysis Presentational tools Graphical displays Multicomponent interventions |
| title | Data visualisation approaches for component network meta-analysis: visualising the data structure |
| title_full | Data visualisation approaches for component network meta-analysis: visualising the data structure |
| title_fullStr | Data visualisation approaches for component network meta-analysis: visualising the data structure |
| title_full_unstemmed | Data visualisation approaches for component network meta-analysis: visualising the data structure |
| title_short | Data visualisation approaches for component network meta-analysis: visualising the data structure |
| title_sort | data visualisation approaches for component network meta analysis visualising the data structure |
| topic | Component network meta-analysis Data visualisation Meta-analysis Presentational tools Graphical displays Multicomponent interventions |
| url | https://doi.org/10.1186/s12874-023-02026-z |
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