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Anales de la Sociedad Mexicana de Oftalmología y Archivos de la Asociación Para Evitar la Ceguera en México

2018, Number 3

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Rev Mex Oftalmol 2018; 92 (3)

Network Meta-analysis

Fau C, Nabzo S, Nasabun V

Language: Spanish
References: 18
Page: 153-159
PDF size: 167.25 Kb.


ABSTRACT

The network meta-analysis (also called mixed comparisons of treatments) is a powerful statistical technique that combines different studies to realize the analysis of multiple treatments or to estimate an indirect effect in the absence of a direct comparison. These studies are carried out by the development of a network of analysis, allowing to calculate the relative effects of all the treatments or interventions included in the network simultaneously and using techniques that estimate the direct and indirect analysis of the evidence. Inductions are comparisons of different treatments using data from different studies and using a comparator in common, either because these studies are not available or are of poor quality or if one wishes to compare numerous alternatives. In the network meta-analysis the mixed treatment comparison is based on a closed- loop network, which provides much more information and is less biased than open loops. Currently in closed loops or cycles, several statistical methods are used for its analysis, but in each study a unique statistical approach is used. The most frequent to date are the Bayesian methods, therefore is more important the analysis of the process of research and network created that obtaining a single weighted final measure. The objective of this narrative review is to describe the fundamental concepts of the network meta-analysis, utility, methodological considerations, the fundamentals of the analysis, the conformation of the network and its main limitations.


REFERENCES

  1. Jansen JP, Trikalinos T, Cappelleri JC, Daw J, Andes S, Eldessouki R, et al. Indirect Treatment Comparison/Network Meta-Analysis Study Questionnaire to Assess Relevance and Credibility to Inform Health Care Decision Making: An ISPOR-AMCP-NPC Good Practice Task Force Report. Value in Health. 2014;17(2):157-73.

  2. Lumley T. Network meta-analysis for indirect treatment comparisons. Stat Med. 2002;21(16):2313-24.

  3. Li T, Puhan MA, Vedula SS, Singh S, Dickersin K. Network meta-analysis- highly attractive but more methodological research is needed. BMC Med. 2011;9:1-5.

  4. Van Valkenhoef G, Tervonen T, Zhao J, De Brock B, Hillege HL, Postmus D. Multicriteria benefit-risk assessment using network meta- analysis. J Clin Epidemiol. 2012;65(4):394-403.

  5. Haynes RB. Of studies, syntheses, synopses, summaries, and systems: the “5S” evolution of information services for evidence-based healthcare decisions. Evid Based Med. 2006;11(6):162-4.

  6. Van der Valk R, Webers CA, Lumley T, Hendrikse F, Prins MH, Schouten JS. Network meta-analysis combined direct and indirect comparisons between glaucoma drugs to rank effectiveness in lowering intraocular pressure. J Clin Epidemiol. 2009;62(12):1279-83.

  7. Hutton B, Salanti G, Caldwell DM, Chaimani A, Schmid CH, Cameron C, et al. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann Intern Med. 2015;162(11):777-84.

  8. Chung H, Lumley T. Graphical exploration of network meta-analysis data: the use of multidimensional scaling. Clin Trials. 2008;5(4):301-7.

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

  10. Zhang J, Xie J, Hou W, Tu X, Xu J, Song F, et al. Mapping the knowledge structure of research on patient adherence: knowledge domain visualization based co-word analysis and social network analysis. PLoS One. 2012;7(4):e34497.

  11. Jones B, Roger J, Lane PW, Lawton A, Fletcher C, Cappelleri JC, et al. Statistical approaches for conducting network meta-analysis in drug development. Pharm Stat. 2011;10(6):523-31.

  12. Song F, Clark A, Bachmann MO, Maas J. Simulation evaluation of statistical properties of methods for indirect and mixed treatment comparisons. BMC Med Res Methodol. 2012;12:138.

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

  14. Catalá-López F, Hutton B, Moher D. La propiedad transitiva en los ensayos clínicos controlados: si B es mejor que A y C es mejor que B, ¿C será mejor que A? Rev Esp Cardiol. 2014;67(8):597-602.

  15. Veroniki AA, Vasiliadis HS, Higgins JP, Salanti G. Evaluation of inconsistency in networks of interventions. Int J Epidemiol. 2013;42(1):332-45.

  16. Song F, Loke YK, Walsh T, Glenny AM, Eastwood AJ, Altman DG. Methodological problems in the use of indirect comparisons for evaluating healthcare interventions: survey of published systematic reviews. BMJ. 2009;338(7700):932-5.

  17. Neupane B, Richer D, Bonner AJ, Kibret T, Beyene J. Network Meta- Analysis Using R: A Review of Currently Available Automated Packages. PLoS One. 2015;10(4):e0123364.

  18. Tobías A, Catalá-López F, Roqué M. Desarrollo de una hoja Excel para metaanálisis de comparaciones indirectas y mixtas. Rev Esp de Salud Pública. 2014;88(1):5-15.




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