Literature DB >> 35146500

Introducing the Treatment Hierarchy Question in Network Meta-Analysis.

Georgia Salanti, Adriani Nikolakopoulou, Orestis Efthimiou, Dimitris Mavridis, Matthias Egger, Ian R White.   

Abstract

Comparative effectiveness research using network meta-analysis can present a hierarchy of competing treatments, from the most to the least preferable option. However, in published reviews, the research question associated with the hierarchy of multiple interventions is typically not clearly defined. Here we introduce the novel notion of a treatment hierarchy question that describes the criterion for choosing a specific treatment over one or more competing alternatives. For example, stakeholders might ask which treatment is most likely to improve mean survival by at least 2 years, or which treatment is associated with the longest mean survival. We discuss the most commonly used ranking metrics (quantities that compare the estimated treatment-specific effects), how the ranking metrics produce a treatment hierarchy, and the type of treatment hierarchy question that each ranking metric can answer. We show that the ranking metrics encompass the uncertainty in the estimation of the treatment effects in different ways, which results in different treatment hierarchies. When using network meta-analyses that aim to rank treatments, investigators should state the treatment hierarchy question they aim to address and employ the appropriate ranking metric to answer it. Following this new proposal will avoid some controversies that have arisen in comparative effectiveness research.
© The Author(s) 2022. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.

Entities:  

Keywords:  multiple treatments; network meta-analysis; probability; ranking; surface under the cumulative ranking curve; treatment hierarchy

Mesh:

Year:  2022        PMID: 35146500      PMCID: PMC9071581          DOI: 10.1093/aje/kwab278

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   5.363


  21 in total

1.  Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool.

Authors:  Georgia Salanti
Journal:  Res Synth Methods       Date:  2012-06-11       Impact factor: 5.273

2.  Uncertainty in Treatment Rankings: Reanalysis of Network Meta-analyses of Randomized Trials.

Authors:  Ludovic Trinquart; Nassima Attiche; Aïida Bafeta; Raphaël Porcher; Philippe Ravaud
Journal:  Ann Intern Med       Date:  2016-04-19       Impact factor: 25.391

3.  Is providing uncertainty intervals in treatment ranking helpful in a network meta-analysis?

Authors:  Areti Angeliki Veroniki; Sharon E Straus; Gerta Rücker; Andrea C Tricco
Journal:  J Clin Epidemiol       Date:  2018-02-10       Impact factor: 6.437

4.  Using decision thresholds for ranking treatments in network meta-analysis results in more informative rankings.

Authors:  Romina Brignardello-Petersen; Bradley C Johnston; Alejandro R Jadad; George Tomlinson
Journal:  J Clin Epidemiol       Date:  2018-02-14       Impact factor: 6.437

5.  Applying Multiple Criteria Decision Analysis to Comparative Benefit-Risk Assessment: Choosing among Statins in Primary Prevention.

Authors:  Tommi Tervonen; Huseyin Naci; Gert van Valkenhoef; Anthony E Ades; Aris Angelis; Hans L Hillege; Douwe Postmus
Journal:  Med Decis Making       Date:  2015-05-18       Impact factor: 2.583

6.  The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations.

Authors:  Brian Hutton; Georgia Salanti; Deborah M Caldwell; Anna Chaimani; Christopher H Schmid; Chris Cameron; John P A Ioannidis; Sharon Straus; Kristian Thorlund; Jeroen P Jansen; Cynthia Mulrow; Ferrán Catalá-López; Peter C Gøtzsche; Kay Dickersin; Isabelle Boutron; Douglas G Altman; David Moher
Journal:  Ann Intern Med       Date:  2015-06-02       Impact factor: 25.391

7.  Bias in identification of the best treatment in a Bayesian network meta-analysis for binary outcome: a simulation study.

Authors:  Taddele Kibret; Danielle Richer; Joseph Beyene
Journal:  Clin Epidemiol       Date:  2014-12-03       Impact factor: 4.790

8.  Evaluating the quality of evidence from a network meta-analysis.

Authors:  Georgia Salanti; Cinzia Del Giovane; Anna Chaimani; Deborah M Caldwell; Julian P T Higgins
Journal:  PLoS One       Date:  2014-07-03       Impact factor: 3.240

9.  CINeMA: An approach for assessing confidence in the results of a network meta-analysis.

Authors:  Adriani Nikolakopoulou; Julian P T Higgins; Theodoros Papakonstantinou; Anna Chaimani; Cinzia Del Giovane; Matthias Egger; Georgia Salanti
Journal:  PLoS Med       Date:  2020-04-03       Impact factor: 11.069

10.  Agreement between ranking metrics in network meta-analysis: an empirical study.

Authors:  Virginia Chiocchia; Adriani Nikolakopoulou; Theodoros Papakonstantinou; Matthias Egger; Georgia Salanti
Journal:  BMJ Open       Date:  2020-08-20       Impact factor: 2.692

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  1 in total

1.  Answering complex hierarchy questions in network meta-analysis.

Authors:  Theodoros Papakonstantinou; Georgia Salanti; Dimitris Mavridis; Gerta Rücker; Guido Schwarzer; Adriani Nikolakopoulou
Journal:  BMC Med Res Methodol       Date:  2022-02-17       Impact factor: 4.615

  1 in total

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