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Network Meta-analysis

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Book cover Health Services Evaluation

Part of the book series: Health Services Research ((HEALTHSR))

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Abstract

The increasing number of alternative treatment options for the same condition created the need to undertake reviews that address complex policy-relevant questions and make inferences about many competing treatments. Such reviews collect data which, under conditions, can be statistically synthesized using network meta-analysis. This chapter presents the basic concepts of indirect and mixed comparison of treatments and presents the statistical models for network meta-analysis and their implementation both theoretically and in examples. The assumption underlying network meta-analysis is extensively discussed and extensions of the models to account for effect modifiers are presented.

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References

  • Baker SG, Kramer BS. The transitive fallacy for randomized trials: if A bests B and B bests C in separate trials, is A better than C? BMC Med Res Methodol. 2002;2:13.

    Article  PubMed  PubMed Central  Google Scholar 

  • Barbui C, Cipriani A, Furukawa TA, et al. Making the best use of available evidence: the case of new generation antidepressants: a response to: are all antidepressants equal? Evid Based Ment Health. 2009;12:101–4.

    Article  PubMed  Google Scholar 

  • Bucher HC, Guyatt GH, Griffith EL, et al. The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. J Clin Epidemiol. 1997;50(6):683–91.

    Article  CAS  PubMed  Google Scholar 

  • Caldwell DM, Ades AE, Higgins JPT. Simultaneous comparison of multiple treatments: combining direct and indirect evidence. BMJ. 2005;331:897–900.

    Article  PubMed  PubMed Central  Google Scholar 

  • Caldwell DM, Welton NJ, Ades AE. Mixed treatment comparison analysis provides internally coherent treatment effect estimates based on overviews of reviews and can reveal inconsistency. J Clin Epidemiol. 2010;6(8):875–82.

    Article  Google Scholar 

  • Chaimani A, Higgins JP, Mavridis D, Spyridonos P, Salanti G. Graphical tools for network meta-analysis in STATA. PLoS One. 2013;8(10):e76654.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cipriani A, Furukawa TA, Salanti G, et al. Comparative efficacy and acceptability of 12 new-generation antidepressants: a multiple-treatments meta-analysis. Lancet. 2009;373:746–58.

    Article  CAS  PubMed  Google Scholar 

  • Cooper NJ, Sutton AJ, Morris D, et al. 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(14):1861–81.

    Article  PubMed  Google Scholar 

  • Cooper NJ, Peters J, Lai MC, et al. How valuable are multiple treatment comparison methods in evidence-based health-care evaluation? Value Health. 2011;14:371–80.

    Article  PubMed  Google Scholar 

  • Dias S, Welton NJ, Caldwell DM, et al. Checking consistency in mixed treatment comparison meta-analysis. Stat Med. 2010;29:932–44.

    Article  CAS  PubMed  Google Scholar 

  • Djulbegovic B, Kumar A, Magazin A, et al. Optimism bias leads to inconclusive results-an empirical study. J Clin Epidemiol. 2011;64:583–93.

    Article  PubMed  Google Scholar 

  • Donegan S, Williamson P, Gamble C, et al. Indirect comparisons: a review of reporting and methodological quality. PLoS One. 2010;5:e11054.

    Article  PubMed  PubMed Central  Google Scholar 

  • Edwards SJ, Clarke MJ, Wordsworth S, et al. Indirect comparisons of treatments based on systematic reviews of randomised controlled trials. Int J Clin Pract. 2009;63:841–54.

    Article  CAS  PubMed  Google Scholar 

  • Eli Lilly and Company. Gemcitabine for the treatment of metastatic breast cancer: Single technology appraisal submission to the National Institute for health and Clinical Excellence. 2006. Available from http://www.nice.org.uk

  • Elliott WJ, Meyer PM. Incident diabetes in clinical trials of antihypertensive drugs: a network meta-analysis. Lancet. 2007;369:201–7.

    Article  CAS  PubMed  Google Scholar 

  • Glenny AM, Altman DG, Song F, et al. Indirect comparisons of competing interventions. Health Technol Assess. 2005;9:26.

    Article  Google Scholar 

  • Guyatt GH, Sackett DL, Sinclair JC, et al. Users’ guides to the medical literature. IX. A method for grading health care recommendations. Evidence-Based Medicine Working Group. JAMA. 1995;274:1800–4.

    Article  CAS  PubMed  Google Scholar 

  • Heres S, Davis J, Maino K, et al. Why olanzapine beats risperidone, risperidone beats quetiapine, and quetiapine beats olanzapine: an exploratory analysis of head-to-head comparison studies of second-generation antipsychotics. Am J Psychiatry. 2006;163:185–94.

    Article  PubMed  Google Scholar 

  • Higgins JPT, Green S. Cochrane handbook for systematic reviews of interventions. 5.0.1 ed. The Cochrane Collaboration; 2008; John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, England.

    Google Scholar 

  • Higgins JPT, Thompson SG. Controlling the risk of spurious findings from meta-regression. Stat Med. 2004;23:1663–82.

    Article  PubMed  Google Scholar 

  • Hoaglin DC, Hawkins N, Jansen JP, et al. Conducting indirect-treatment-comparison and network-meta-analysis studies: report of the ISPOR task force on indirect treatment comparisons good research practices-part 2. Value Health. 2011;14:429–37.

    Article  PubMed  Google Scholar 

  • Hughes S. First “comparison” of prasugrel and ticagrelor. 2010 Sep16. Available from http://www.theheart.org/article/1122713.do. Accessed 27 Apr 2011.

  • Jackson D, Riley R, White IR. Multivariate meta-analysis: potential and promise. Stat Med. 2011;30:2481–98.

    Article  PubMed  PubMed Central  Google Scholar 

  • Jansen JP, Schmid CH, Salanti G. Directed acyclic graphs can help understand bias in indirect and mixed treatment comparisons. J Clin Epidemiol. 2012;65:798–807.

    Article  PubMed  Google Scholar 

  • Jones A, Takeda A, Tan SC, Cooper K, Loveman E, Clegg A, Murray N. Gemcitabine for metastatic breast cancer: evidence review group report. 2006. Available from www.nice.org.uk

  • Lambert PC, Sutton AJ, Burton PR, Abrams KR, et al. How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS. Stat Med. 2005;24:2401–28.

    Article  PubMed  Google Scholar 

  • Lu G, Ades AE. Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med. 2004;23(20):3105–24. PMID: 15449338”

    Article  CAS  PubMed  Google Scholar 

  • Lu G, Ades AE. Assessing evidence inconsistency in mixed treatment comparisons. J Am Stat Assoc. 2006;101:447–59.

    Article  CAS  Google Scholar 

  • Lu G, Ades AE. Modeling between-trial variance structure in mixed treatment comparisons. Biostatistics. 2009;10(4):792–805.

    Article  PubMed  Google Scholar 

  • McAlister FA, Laupacis A, Wells GA, et al. Users’ guides to the medical literature: XIX. Applying clinical trial results B. Guidelines for determining whether a drug is exerting (more than) a class effect. JAMA. 1999;282:1371–7.

    Article  CAS  PubMed  Google Scholar 

  • Mills EJ, Ghement I, O’Regan C, et al. Estimating the power of indirect comparisons: a simulation study. PLoS One. 2011;6:e16237.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • NICE. Methods for the development of NICE public health guidance. 2nd ed. Evidence Synthesis National Institute of Health and Clinical Excellence; 2008.

    Google Scholar 

  • O’Regan C, Ghement I, Eyawo O, et al. Incorporating multiple interventions in meta-analysis: an evaluation of the mixed treatment comparison with the adjusted indirect comparison. Trials. 2009;10:86.

    Article  PubMed  PubMed Central  Google Scholar 

  • PBAC. Report of the indirect comparisons working group to the pharmaceutical benefits advisory committee: assessing indirect comparisons. Pharmaceutical Benefits Advisory Committee; 2008. http://www.health.gov.au/internet/main/publishing.nsf/Content/B11E8EF19B358E39CA25754B000A9C07/$File/ICWG%20Report%20FINAL2.pdf

  • Piccini JP, Kong DF. Mixed treatment comparisons for atrial fibrillation: evidence network or bewildering entanglement? Europace. 2011;13:295–6.

    Article  PubMed  Google Scholar 

  • Riley RD. Multivariate meta-analysis: the effect of ignoring within-study correlation. J R Stat Soc Ser A. 2009;172:789–811.

    Article  Google Scholar 

  • Salanti G, Marinho V, Higgins JP. A case study of multiple-treatments meta-analysis demonstrates that covariates should be considered. J Clin Epidemiol. 2009;62:857–64.

    Article  PubMed  Google Scholar 

  • Salanti G, Dias S, Welton NJ, et al. Evaluating novel agent effects in multiple-treatments meta-regression. Stat Med. 2010;29:2369–83.

    PubMed  Google Scholar 

  • Soares HP, Kumar A, Daniels S, et al. Evaluation of new treatments in radiation oncology: are they better than standard treatments? JAMA. 2005;293:970–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Song F, Altman D, Glenny AM, et al. Validity of indirect comparison for estimating efficacy of competing interventions: empirical evidence from published meta-analyses. BMJ. 2003;326:472.

    Article  PubMed  PubMed Central  Google Scholar 

  • Song F, Loke YK, Walsh T, et al. Methodological problems in the use of indirect comparisons for evaluating healthcare interventions: survey of published systematic reviews. BMJ. 2009;338:b1147.

    Article  PubMed  PubMed Central  Google Scholar 

  • Song F, Xiong T, Parekh-Bhurke S, et al. Inconsistency between direct and indirect comparisons of competing interventions: meta-epidemiological study. BMJ. 2011;343:d4909.

    Article  PubMed  PubMed Central  Google Scholar 

  • Spiegelhalter DJ, Best NG, Bradley PC, et al. Bayesian measures of model complexity and fit. J R Stat Soc Ser B. 2002;64:583–639.

    Article  Google Scholar 

  • Spiegelhalter DJ, Abrams KR, Myles PJ. Bayesian approaches to clinical trials and health-care evaluation. Chichester: Wiley; 2004.

    Google Scholar 

  • Sutton AJ, Abrams KR. Bayesian methods in meta-analysis and evidence synthesis. Stat Methods Med Res. 2001;10:277–303.

    Article  CAS  PubMed  Google Scholar 

  • Thijs V, Lemmens R, Fieuws S. Network meta-analysis: simultaneous meta-analysis of common antiplatelet regimens after transient ischaemic attack or stroke. Eur Heart J. 2008;29:1086–92.

    Article  PubMed  Google Scholar 

  • Uhtman OA, Abdulmalik J. Comparative efficacy and acceptability of pharmacotherapeutic agents for anxiety disorders in children and adolescents: a mixed treatment comparison meta-analysis. Cur Med Res Opin. 2010;26(1):53–9.

    Article  Google Scholar 

  • Viechtbauer W. Confidence intervals for the amount of heterogeneity in meta-analysis. Stat Med. 2007;26:37–52.

    Article  PubMed  Google Scholar 

  • Warn DE, Thompson SG, Spiegelhalter DJ. Bayesian random effects meta-analysis of trials with binary outcomes: methods for the absolute risk difference and relative risk scales. Stat Med. 2002;21:1601–23.

    Article  CAS  PubMed  Google Scholar 

  • Wells GA, Sultan SA, Chen L, et al. Indirect evidence: indirect treatment comparisons in meta-analysis. Ottawa: Canadian Agency for Drugs and Technologies in Health; 2009.

    Google Scholar 

  • White IR. Multivariate random-effects meta-regression: updates to mvmeta. Stata J. 2011;11(2):255–70.

    Article  Google Scholar 

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Acknowledgments

GS and AC received funding from the European Research Council (ERC starting grant IMMA 260559). DC is supported by an UK MRC Population Health Scientist Fellowship (G0902118). JPTH is funded by Medical Research Council grant U105285807.

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Correspondence to Georgia Salanti .

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Salanti, G., Caldwell, D., Chaimani, A., Higgins, J. (2019). Network Meta-analysis. In: Levy, A., Goring, S., Gatsonis, C., Sobolev, B., van Ginneken, E., Busse, R. (eds) Health Services Evaluation. Health Services Research. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-8715-3_36

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  • DOI: https://doi.org/10.1007/978-1-4939-8715-3_36

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4939-8714-6

  • Online ISBN: 978-1-4939-8715-3

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