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Published in: BMC Medicine 1/2013

Open Access 01-12-2013 | Debate

Is network meta-analysis as valid as standard pairwise meta-analysis? It all depends on the distribution of effect modifiers

Authors: Jeroen P Jansen, Huseyin Naci

Published in: BMC Medicine | Issue 1/2013

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Abstract

Background

In the last decade, network meta-analysis of randomized controlled trials has been introduced as an extension of pairwise meta-analysis. The advantage of network meta-analysis over standard pairwise meta-analysis is that it facilitates indirect comparisons of multiple interventions that have not been studied in a head-to-head fashion. Although assumptions underlying pairwise meta-analyses are well understood, those concerning network meta-analyses are perceived to be more complex and prone to misinterpretation.

Discussion

In this paper, we aim to provide a basic explanation when network meta-analysis is as valid as pairwise meta-analysis. We focus on the primary role of effect modifiers, which are study and patient characteristics associated with treatment effects. Because network meta-analysis includes different trials comparing different interventions, the distribution of effect modifiers cannot only vary across studies for a particular comparison (as with standard pairwise meta-analysis, causing heterogeneity), but also between comparisons (causing inconsistency). If there is an imbalance in the distribution of effect modifiers between different types of direct comparisons, the related indirect comparisons will be biased. If it can be assumed that this is not the case, network meta-analysis is as valid as pairwise meta-analysis.

Summary

The validity of network meta-analysis is based on the underlying assumption that there is no imbalance in the distribution of effect modifiers across the different types of direct treatment comparisons, regardless of the structure of the evidence network.
Appendix
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Literature
1.
go back to reference Inthout J, Ioannidis JP, Borm GF: Obtaining evidence by a single well-powered trial or several modestly powered trials. Stat Methods Med Res. In press Inthout J, Ioannidis JP, Borm GF: Obtaining evidence by a single well-powered trial or several modestly powered trials. Stat Methods Med Res. In press
2.
go back to reference Caldwell DM, Ades AE, Higgins JPT: Simultaneous comparison of multiple treatments: combining direct and indirect evidence. BMJ. 2005, 331: 897-900. 10.1136/bmj.331.7521.897.CrossRefPubMedPubMedCentral Caldwell DM, Ades AE, Higgins JPT: Simultaneous comparison of multiple treatments: combining direct and indirect evidence. BMJ. 2005, 331: 897-900. 10.1136/bmj.331.7521.897.CrossRefPubMedPubMedCentral
3.
go back to reference Sutton A, Ades AE, Cooper N, Abrams K: Use of indirect and mixed treatment comparisons for technology assessment. PharmacoEconomics. 2008, 26: 753-767. 10.2165/00019053-200826090-00006.CrossRefPubMed Sutton A, Ades AE, Cooper N, Abrams K: Use of indirect and mixed treatment comparisons for technology assessment. PharmacoEconomics. 2008, 26: 753-767. 10.2165/00019053-200826090-00006.CrossRefPubMed
4.
go back to reference Bucher HC, Guyatt GH, Griffith LE, Walter SD: The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. J ClinEpidemiol. 1997, 50: 683-691. Bucher HC, Guyatt GH, Griffith LE, Walter SD: The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. J ClinEpidemiol. 1997, 50: 683-691.
5.
go back to reference Lumley T: Network meta-analysis for indirect treatment comparisons. Stat Med. 2002, 21: 2313-2324. 10.1002/sim.1201.CrossRefPubMed Lumley T: Network meta-analysis for indirect treatment comparisons. Stat Med. 2002, 21: 2313-2324. 10.1002/sim.1201.CrossRefPubMed
6.
go back to reference Madan J, Stevenson MD, Cooper KL, Ades AE, Whyte S, Akehurst R: Consistency between direct and indirect trial evidence: is direct evidence always more reliable?. Value Health. 2011, 14: 953-960. 10.1016/j.jval.2011.05.042.CrossRefPubMed Madan J, Stevenson MD, Cooper KL, Ades AE, Whyte S, Akehurst R: Consistency between direct and indirect trial evidence: is direct evidence always more reliable?. Value Health. 2011, 14: 953-960. 10.1016/j.jval.2011.05.042.CrossRefPubMed
7.
go back to reference Song F, Harvey I, Lilford R: Adjusted indirect comparison may be less biased than direct comparison for evaluating new pharmaceutical interventions. J ClinEpidemiol. 2008, 61: 455-463. Song F, Harvey I, Lilford R: Adjusted indirect comparison may be less biased than direct comparison for evaluating new pharmaceutical interventions. J ClinEpidemiol. 2008, 61: 455-463.
8.
go back to reference Salanti G, Higgins JP, Ades AE, Ioannidis JP: Evaluation of networks of randomized trials. Stat Methods Med Res. 2008, 17: 279-301.CrossRefPubMed Salanti G, Higgins JP, Ades AE, Ioannidis JP: Evaluation of networks of randomized trials. Stat Methods Med Res. 2008, 17: 279-301.CrossRefPubMed
9.
go back to reference Mills EJ, Ioannidis JP, Thorlund K, Schunemann HJ, Puhan MA, Guyatt GH: How to use an article reporting a multiple treatment comparison meta-analysis. JAMA. 2012, 308: 1246-1253. 10.1001/2012.jama.11228.CrossRefPubMed Mills EJ, Ioannidis JP, Thorlund K, Schunemann HJ, Puhan MA, Guyatt GH: How to use an article reporting a multiple treatment comparison meta-analysis. JAMA. 2012, 308: 1246-1253. 10.1001/2012.jama.11228.CrossRefPubMed
10.
go back to reference Li T, Puhan MA, Vedula SS, Singh S, Dickersin K: Network meta-analysis-highly attractive but more methodological research is needed. BMC Med. 2011, 9: 79-10.1186/1741-7015-9-79.CrossRefPubMedPubMedCentral Li T, Puhan MA, Vedula SS, Singh S, Dickersin K: Network meta-analysis-highly attractive but more methodological research is needed. BMC Med. 2011, 9: 79-10.1186/1741-7015-9-79.CrossRefPubMedPubMedCentral
11.
go back to reference Naci H, Fleurence R: Using indirect evidence to determine the comparative effectiveness of prescription drugs: do benefits outweigh risks?. Health Outcomes Res Med. 2011, 2: e241-e249. 10.1016/j.ehrm.2011.10.001.CrossRef Naci H, Fleurence R: Using indirect evidence to determine the comparative effectiveness of prescription drugs: do benefits outweigh risks?. Health Outcomes Res Med. 2011, 2: e241-e249. 10.1016/j.ehrm.2011.10.001.CrossRef
12.
go back to reference Savović J, Jones HE, Altman DG, Harris RJ, Jüni P, Pildal J, Als-Nielsen B, Balk EM, Gluud C, Gluud LL, Ioannidis JP, Schulz KF, Beynon R, Welton NJ, Wood L, Moher D, Deeks JJ, Sterne JA: Influence of reported study design characteristics on intervention effect estimates from randomized, controlled trials. Ann Intern Med. 2012, 157: 429-438.CrossRefPubMed Savović J, Jones HE, Altman DG, Harris RJ, Jüni P, Pildal J, Als-Nielsen B, Balk EM, Gluud C, Gluud LL, Ioannidis JP, Schulz KF, Beynon R, Welton NJ, Wood L, Moher D, Deeks JJ, Sterne JA: Influence of reported study design characteristics on intervention effect estimates from randomized, controlled trials. Ann Intern Med. 2012, 157: 429-438.CrossRefPubMed
13.
go back to reference Higgins JP, Thompson SG: Quantifying heterogeneity in a meta-analysis. Stat Med. 2002, 21: 1539-1558. 10.1002/sim.1186.CrossRefPubMed Higgins JP, Thompson SG: Quantifying heterogeneity in a meta-analysis. Stat Med. 2002, 21: 1539-1558. 10.1002/sim.1186.CrossRefPubMed
14.
go back to reference Sutton AJ, Abrams KR, Jones DR, Sheldon TA, Song F: Methods for Meta-Analysis in Medical Research. 2000, London, UK: Wiley Sutton AJ, Abrams KR, Jones DR, Sheldon TA, Song F: Methods for Meta-Analysis in Medical Research. 2000, London, UK: Wiley
15.
go back to reference Thompson SG: Systematic Review: Why sources of heterogeneity in meta-analysis should be investigated. BMJ. 1994, 309: 1351-1355. 10.1136/bmj.309.6965.1351.CrossRefPubMedPubMedCentral Thompson SG: Systematic Review: Why sources of heterogeneity in meta-analysis should be investigated. BMJ. 1994, 309: 1351-1355. 10.1136/bmj.309.6965.1351.CrossRefPubMedPubMedCentral
16.
go back to reference Jansen JP, Schmid CH, Salanti G: Directed acyclic graphs can help understand bias in indirect and mixed treatment comparisons. J ClinEpidemiol. 2012, 65: 798-807. Jansen JP, Schmid CH, Salanti G: Directed acyclic graphs can help understand bias in indirect and mixed treatment comparisons. J ClinEpidemiol. 2012, 65: 798-807.
17.
go back to reference Song F, Loke YK, Walsh T, Glenny A-M, Eastwood AJ, Altman DG: Methodological problems in the use of indirect comparisons for evaluating healthcare interventions: survey of published systematic reviews. BMJ. 2009, 19: 338. Song F, Loke YK, Walsh T, Glenny A-M, Eastwood AJ, Altman DG: Methodological problems in the use of indirect comparisons for evaluating healthcare interventions: survey of published systematic reviews. BMJ. 2009, 19: 338.
18.
go back to reference Lu G, Ades AE: Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med. 2004, 23: 3105-3124. 10.1002/sim.1875.CrossRefPubMed Lu G, Ades AE: Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med. 2004, 23: 3105-3124. 10.1002/sim.1875.CrossRefPubMed
19.
go back to reference Lu G, Ades AE: Assessing evidence inconsistency in mixed treatment comparisons. J Am Stat Assoc. 2006, 101: 447-459. 10.1198/016214505000001302.CrossRef Lu G, Ades AE: Assessing evidence inconsistency in mixed treatment comparisons. J Am Stat Assoc. 2006, 101: 447-459. 10.1198/016214505000001302.CrossRef
20.
go back to reference Higgins JPT, Jackson D, Barrett JK, Lu G, Ades AE, White IR: Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Res Synth Methods. 2012, 3: 98-110. 10.1002/jrsm.1044.CrossRefPubMedPubMedCentral Higgins JPT, Jackson D, Barrett JK, Lu G, Ades AE, White IR: Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Res Synth Methods. 2012, 3: 98-110. 10.1002/jrsm.1044.CrossRefPubMedPubMedCentral
21.
go back to reference Jansen JP, Fleurence R, Devine B, Itzler R, Barrett A, Hawkins N, Lee K, Boersma C, Annemans L, Cappelleri JC: Interpreting indirect treatment comparisons and network meta-analysis for health-care decision making: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: Part 1. Value Health. 2011, 14: 417-428. 10.1016/j.jval.2011.04.002.CrossRefPubMed Jansen JP, Fleurence R, Devine B, Itzler R, Barrett A, Hawkins N, Lee K, Boersma C, Annemans L, Cappelleri JC: Interpreting indirect treatment comparisons and network meta-analysis for health-care decision making: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: Part 1. Value Health. 2011, 14: 417-428. 10.1016/j.jval.2011.04.002.CrossRefPubMed
22.
go back to reference Thompson SG, Higgins JPT: How should meta-regression analyses be undertaken and interpreted?. Stat Med. 2002, 21: 1559-1573. 10.1002/sim.1187.CrossRefPubMed Thompson SG, Higgins JPT: How should meta-regression analyses be undertaken and interpreted?. Stat Med. 2002, 21: 1559-1573. 10.1002/sim.1187.CrossRefPubMed
23.
go back to reference Cooper NJ, Sutton AJ, Morris D, Ades AE, Welton NJ: Addressing between-study heterogeneity and inconsistency in mixed treatment comparisons: application to stroke prevention treatments in individuals with non-rheumatic atrial fibrillation. Stat Med. 2009, 28: 1861-1881. 10.1002/sim.3594.CrossRefPubMed Cooper NJ, Sutton AJ, Morris D, Ades AE, Welton NJ: Addressing between-study heterogeneity and inconsistency in mixed treatment comparisons: application to stroke prevention treatments in individuals with non-rheumatic atrial fibrillation. Stat Med. 2009, 28: 1861-1881. 10.1002/sim.3594.CrossRefPubMed
24.
go back to reference Salanti G, Marinho V, Higgins JP: A case study of multiple-treatments meta-analysis demonstrates that covariates should be considered. J ClinEpidemiol. 2009, 62: 857-864. Salanti G, Marinho V, Higgins JP: A case study of multiple-treatments meta-analysis demonstrates that covariates should be considered. J ClinEpidemiol. 2009, 62: 857-864.
25.
go back to reference Jansen JP: Network meta-analysis of individual and aggregate level data. Res Synth Methods. 2012, 3: 177-190. 10.1002/jrsm.1048.CrossRefPubMed Jansen JP: Network meta-analysis of individual and aggregate level data. Res Synth Methods. 2012, 3: 177-190. 10.1002/jrsm.1048.CrossRefPubMed
26.
go back to reference Saramago P, Sutton AJ, Cooper NJ, Manca A: Mixed treatment comparisons using aggregate and individual participant level data. Stat Med. 2012, 31: 3516-3536. 10.1002/sim.5442.CrossRefPubMed Saramago P, Sutton AJ, Cooper NJ, Manca A: Mixed treatment comparisons using aggregate and individual participant level data. Stat Med. 2012, 31: 3516-3536. 10.1002/sim.5442.CrossRefPubMed
Metadata
Title
Is network meta-analysis as valid as standard pairwise meta-analysis? It all depends on the distribution of effect modifiers
Authors
Jeroen P Jansen
Huseyin Naci
Publication date
01-12-2013
Publisher
BioMed Central
Published in
BMC Medicine / Issue 1/2013
Electronic ISSN: 1741-7015
DOI
https://doi.org/10.1186/1741-7015-11-159

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