A graphical tool for locating inconsistency in network meta-analyses
Background In network meta-analyses, several treatments can be compared by connecting evidence from clinical trials that have investigated two or more treatments. The resulting trial network allows estimating the relative effects of all pairs of treatments taking indirect evidence into account. For...
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| Published in | BMC medical research methodology Vol. 13; no. 1; p. 35 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
09.03.2013
BioMed Central Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1471-2288 1471-2288 |
| DOI | 10.1186/1471-2288-13-35 |
Cover
| Abstract | Background
In network meta-analyses, several treatments can be compared by connecting evidence from clinical trials that have investigated two or more treatments. The resulting trial network allows estimating the relative effects of all pairs of treatments taking indirect evidence into account. For a valid analysis of the network, consistent information from different pathways is assumed. Consistency can be checked by contrasting effect estimates from direct comparisons with the evidence of the remaining network. Unfortunately, one deviating direct comparison may have side effects on the network estimates of others, thus producing hot spots of inconsistency.
Methods
We provide a tool, the net heat plot, to render transparent which direct comparisons drive each network estimate and to display hot spots of inconsistency: this permits singling out which of the suspicious direct comparisons are sufficient to explain the presence of inconsistency. We base our methods on fixed-effects models. For disclosure of potential drivers, the plot comprises the contribution of each direct estimate to network estimates resulting from regression diagnostics. In combination, we show heat colors corresponding to the change in agreement between direct and indirect estimate when relaxing the assumption of consistency for one direct comparison. A clustering procedure is applied to the heat matrix in order to find hot spots of inconsistency.
Results
The method is shown to work with several examples, which are constructed by perturbing the effect of single study designs, and with two published network meta-analyses. Once the possible sources of inconsistencies are identified, our method also reveals which network estimates they affect.
Conclusion
Our proposal is seen to be useful for identifying sources of inconsistencies in the network together with the interrelatedness of effect estimates. It opens the way for a further analysis based on subject matter considerations. |
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| AbstractList | In network meta-analyses, several treatments can be compared by connecting evidence from clinical trials that have investigated two or more treatments. The resulting trial network allows estimating the relative effects of all pairs of treatments taking indirect evidence into account. For a valid analysis of the network, consistent information from different pathways is assumed. Consistency can be checked by contrasting effect estimates from direct comparisons with the evidence of the remaining network. Unfortunately, one deviating direct comparison may have side effects on the network estimates of others, thus producing hot spots of inconsistency.BACKGROUNDIn network meta-analyses, several treatments can be compared by connecting evidence from clinical trials that have investigated two or more treatments. The resulting trial network allows estimating the relative effects of all pairs of treatments taking indirect evidence into account. For a valid analysis of the network, consistent information from different pathways is assumed. Consistency can be checked by contrasting effect estimates from direct comparisons with the evidence of the remaining network. Unfortunately, one deviating direct comparison may have side effects on the network estimates of others, thus producing hot spots of inconsistency.We provide a tool, the net heat plot, to render transparent which direct comparisons drive each network estimate and to display hot spots of inconsistency: this permits singling out which of the suspicious direct comparisons are sufficient to explain the presence of inconsistency. We base our methods on fixed-effects models. For disclosure of potential drivers, the plot comprises the contribution of each direct estimate to network estimates resulting from regression diagnostics. In combination, we show heat colors corresponding to the change in agreement between direct and indirect estimate when relaxing the assumption of consistency for one direct comparison. A clustering procedure is applied to the heat matrix in order to find hot spots of inconsistency.METHODSWe provide a tool, the net heat plot, to render transparent which direct comparisons drive each network estimate and to display hot spots of inconsistency: this permits singling out which of the suspicious direct comparisons are sufficient to explain the presence of inconsistency. We base our methods on fixed-effects models. For disclosure of potential drivers, the plot comprises the contribution of each direct estimate to network estimates resulting from regression diagnostics. In combination, we show heat colors corresponding to the change in agreement between direct and indirect estimate when relaxing the assumption of consistency for one direct comparison. A clustering procedure is applied to the heat matrix in order to find hot spots of inconsistency.The method is shown to work with several examples, which are constructed by perturbing the effect of single study designs, and with two published network meta-analyses. Once the possible sources of inconsistencies are identified, our method also reveals which network estimates they affect.RESULTSThe method is shown to work with several examples, which are constructed by perturbing the effect of single study designs, and with two published network meta-analyses. Once the possible sources of inconsistencies are identified, our method also reveals which network estimates they affect.Our proposal is seen to be useful for identifying sources of inconsistencies in the network together with the interrelatedness of effect estimates. It opens the way for a further analysis based on subject matter considerations.CONCLUSIONOur proposal is seen to be useful for identifying sources of inconsistencies in the network together with the interrelatedness of effect estimates. It opens the way for a further analysis based on subject matter considerations. Background In network meta-analyses, several treatments can be compared by connecting evidence from clinical trials that have investigated two or more treatments. The resulting trial network allows estimating the relative effects of all pairs of treatments taking indirect evidence into account. For a valid analysis of the network, consistent information from different pathways is assumed. Consistency can be checked by contrasting effect estimates from direct comparisons with the evidence of the remaining network. Unfortunately, one deviating direct comparison may have side effects on the network estimates of others, thus producing hot spots of inconsistency. Methods We provide a tool, the net heat plot, to render transparent which direct comparisons drive each network estimate and to display hot spots of inconsistency: this permits singling out which of the suspicious direct comparisons are sufficient to explain the presence of inconsistency. We base our methods on fixed-effects models. For disclosure of potential drivers, the plot comprises the contribution of each direct estimate to network estimates resulting from regression diagnostics. In combination, we show heat colors corresponding to the change in agreement between direct and indirect estimate when relaxing the assumption of consistency for one direct comparison. A clustering procedure is applied to the heat matrix in order to find hot spots of inconsistency. Results The method is shown to work with several examples, which are constructed by perturbing the effect of single study designs, and with two published network meta-analyses. Once the possible sources of inconsistencies are identified, our method also reveals which network estimates they affect. Conclusion Our proposal is seen to be useful for identifying sources of inconsistencies in the network together with the interrelatedness of effect estimates. It opens the way for a further analysis based on subject matter considerations. Keywords: Network meta-analysis, Inconsistency, Cochran's Q, Hat matrix Doc number: 35 Abstract Background: In network meta-analyses, several treatments can be compared by connecting evidence from clinical trials that have investigated two or more treatments. The resulting trial network allows estimating the relative effects of all pairs of treatments taking indirect evidence into account. For a valid analysis of the network, consistent information from different pathways is assumed. Consistency can be checked by contrasting effect estimates from direct comparisons with the evidence of the remaining network. Unfortunately, one deviating direct comparison may have side effects on the network estimates of others, thus producing hot spots of inconsistency. Methods: We provide a tool, the net heat plot, to render transparent which direct comparisons drive each network estimate and to display hot spots of inconsistency: this permits singling out which of the suspicious direct comparisons are sufficient to explain the presence of inconsistency. We base our methods on fixed-effects models. For disclosure of potential drivers, the plot comprises the contribution of each direct estimate to network estimates resulting from regression diagnostics. In combination, we show heat colors corresponding to the change in agreement between direct and indirect estimate when relaxing the assumption of consistency for one direct comparison. A clustering procedure is applied to the heat matrix in order to find hot spots of inconsistency. Results: The method is shown to work with several examples, which are constructed by perturbing the effect of single study designs, and with two published network meta-analyses. Once the possible sources of inconsistencies are identified, our method also reveals which network estimates they affect. Conclusion: Our proposal is seen to be useful for identifying sources of inconsistencies in the network together with the interrelatedness of effect estimates. It opens the way for a further analysis based on subject matter considerations. Background In network meta-analyses, several treatments can be compared by connecting evidence from clinical trials that have investigated two or more treatments. The resulting trial network allows estimating the relative effects of all pairs of treatments taking indirect evidence into account. For a valid analysis of the network, consistent information from different pathways is assumed. Consistency can be checked by contrasting effect estimates from direct comparisons with the evidence of the remaining network. Unfortunately, one deviating direct comparison may have side effects on the network estimates of others, thus producing hot spots of inconsistency. Methods We provide a tool, the net heat plot, to render transparent which direct comparisons drive each network estimate and to display hot spots of inconsistency: this permits singling out which of the suspicious direct comparisons are sufficient to explain the presence of inconsistency. We base our methods on fixed-effects models. For disclosure of potential drivers, the plot comprises the contribution of each direct estimate to network estimates resulting from regression diagnostics. In combination, we show heat colors corresponding to the change in agreement between direct and indirect estimate when relaxing the assumption of consistency for one direct comparison. A clustering procedure is applied to the heat matrix in order to find hot spots of inconsistency. Results The method is shown to work with several examples, which are constructed by perturbing the effect of single study designs, and with two published network meta-analyses. Once the possible sources of inconsistencies are identified, our method also reveals which network estimates they affect. Conclusion Our proposal is seen to be useful for identifying sources of inconsistencies in the network together with the interrelatedness of effect estimates. It opens the way for a further analysis based on subject matter considerations. In network meta-analyses, several treatments can be compared by connecting evidence from clinical trials that have investigated two or more treatments. The resulting trial network allows estimating the relative effects of all pairs of treatments taking indirect evidence into account. For a valid analysis of the network, consistent information from different pathways is assumed. Consistency can be checked by contrasting effect estimates from direct comparisons with the evidence of the remaining network. Unfortunately, one deviating direct comparison may have side effects on the network estimates of others, thus producing hot spots of inconsistency. We provide a tool, the net heat plot, to render transparent which direct comparisons drive each network estimate and to display hot spots of inconsistency: this permits singling out which of the suspicious direct comparisons are sufficient to explain the presence of inconsistency. We base our methods on fixed-effects models. For disclosure of potential drivers, the plot comprises the contribution of each direct estimate to network estimates resulting from regression diagnostics. In combination, we show heat colors corresponding to the change in agreement between direct and indirect estimate when relaxing the assumption of consistency for one direct comparison. A clustering procedure is applied to the heat matrix in order to find hot spots of inconsistency. The method is shown to work with several examples, which are constructed by perturbing the effect of single study designs, and with two published network meta-analyses. Once the possible sources of inconsistencies are identified, our method also reveals which network estimates they affect. Our proposal is seen to be useful for identifying sources of inconsistencies in the network together with the interrelatedness of effect estimates. It opens the way for a further analysis based on subject matter considerations. BACKGROUND: In network meta-analyses, several treatments can be compared by connecting evidence from clinical trials that have investigated two or more treatments. The resulting trial network allows estimating the relative effects of all pairs of treatments taking indirect evidence into account. For a valid analysis of the network, consistent information from different pathways is assumed. Consistency can be checked by contrasting effect estimates from direct comparisons with the evidence of the remaining network. Unfortunately, one deviating direct comparison may have side effects on the network estimates of others, thus producing hot spots of inconsistency. METHODS: We provide a tool, the net heat plot, to render transparent which direct comparisons drive each network estimate and to display hot spots of inconsistency: this permits singling out which of the suspicious direct comparisons are sufficient to explain the presence of inconsistency. We base our methods on fixed-effects models. For disclosure of potential drivers, the plot comprises the contribution of each direct estimate to network estimates resulting from regression diagnostics. In combination, we show heat colors corresponding to the change in agreement between direct and indirect estimate when relaxing the assumption of consistency for one direct comparison. A clustering procedure is applied to the heat matrix in order to find hot spots of inconsistency. RESULTS: The method is shown to work with several examples, which are constructed by perturbing the effect of single study designs, and with two published network meta-analyses. Once the possible sources of inconsistencies are identified, our method also reveals which network estimates they affect. CONCLUSION: Our proposal is seen to be useful for identifying sources of inconsistencies in the network together with the interrelatedness of effect estimates. It opens the way for a further analysis based on subject matter considerations. In network meta-analyses, several treatments can be compared by connecting evidence from clinical trials that have investigated two or more treatments. The resulting trial network allows estimating the relative effects of all pairs of treatments taking indirect evidence into account. For a valid analysis of the network, consistent information from different pathways is assumed. Consistency can be checked by contrasting effect estimates from direct comparisons with the evidence of the remaining network. Unfortunately, one deviating direct comparison may have side effects on the network estimates of others, thus producing hot spots of inconsistency. We provide a tool, the net heat plot, to render transparent which direct comparisons drive each network estimate and to display hot spots of inconsistency: this permits singling out which of the suspicious direct comparisons are sufficient to explain the presence of inconsistency. We base our methods on fixed-effects models. For disclosure of potential drivers, the plot comprises the contribution of each direct estimate to network estimates resulting from regression diagnostics. In combination, we show heat colors corresponding to the change in agreement between direct and indirect estimate when relaxing the assumption of consistency for one direct comparison. A clustering procedure is applied to the heat matrix in order to find hot spots of inconsistency. The method is shown to work with several examples, which are constructed by perturbing the effect of single study designs, and with two published network meta-analyses. Once the possible sources of inconsistencies are identified, our method also reveals which network estimates they affect. Our proposal is seen to be useful for identifying sources of inconsistencies in the network together with the interrelatedness of effect estimates. It opens the way for a further analysis based on subject matter considerations. |
| ArticleNumber | 35 |
| Audience | Academic |
| Author | König, Jochem Krahn, Ulrike Binder, Harald |
| AuthorAffiliation | 1 Division Medical Biometry, Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center Johannes Gutenberg University Mainz, Obere Zahlbacher Str. 69, 55131 Mainz, Germany |
| AuthorAffiliation_xml | – name: 1 Division Medical Biometry, Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center Johannes Gutenberg University Mainz, Obere Zahlbacher Str. 69, 55131 Mainz, Germany |
| Author_xml | – sequence: 1 givenname: Ulrike surname: Krahn fullname: Krahn, Ulrike email: ulrike.krahn@unimedizin-mainz.de organization: Division Medical Biometry, Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center Johannes Gutenberg University Mainz – sequence: 2 givenname: Harald surname: Binder fullname: Binder, Harald organization: Division Medical Biometry, Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center Johannes Gutenberg University Mainz – sequence: 3 givenname: Jochem surname: König fullname: König, Jochem organization: Division Medical Biometry, Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center Johannes Gutenberg University Mainz |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/23496991$$D View this record in MEDLINE/PubMed |
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| Snippet | Background
In network meta-analyses, several treatments can be compared by connecting evidence from clinical trials that have investigated two or more... In network meta-analyses, several treatments can be compared by connecting evidence from clinical trials that have investigated two or more treatments. The... Background In network meta-analyses, several treatments can be compared by connecting evidence from clinical trials that have investigated two or more... Doc number: 35 Abstract Background: In network meta-analyses, several treatments can be compared by connecting evidence from clinical trials that have... BACKGROUND: In network meta-analyses, several treatments can be compared by connecting evidence from clinical trials that have investigated two or more... |
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| StartPage | 35 |
| SubjectTerms | Computer Communication Networks Computer Graphics Data analysis Estimation theory Generalized linear models Health Sciences Humans Maximum likelihood method Medical research Medicine Medicine & Public Health Medicine, Experimental Meta-analysis Meta-Analysis as Topic Research Article Statistical Theory and Methods statistics and modelling Statistics for Life Sciences Studies Theory of Medicine/Bioethics Treatment outcome |
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| Title | A graphical tool for locating inconsistency in network meta-analyses |
| URI | https://link.springer.com/article/10.1186/1471-2288-13-35 https://www.ncbi.nlm.nih.gov/pubmed/23496991 https://www.proquest.com/docview/1348958869 https://www.proquest.com/docview/1349092404 http://dx.doi.org/10.1186/1471-2288-13-35 https://pubmed.ncbi.nlm.nih.gov/PMC3644268 |
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