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Network meta-analysis for decision making in health sciences

Explore the challenges and benefits of network meta-analysis for informed health decisions in patient care. Learn how to compare and rank various interventions with accuracy and consistency. Understand the steps and methods involved in network meta-analysis.

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Network meta-analysis for decision making in health sciences

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  1. Network meta-analysis for decision making in health sciences Alejandro G. Gonzalez Garay, M.D., Ms.C, Ph.D. National Institute of Pediatrics

  2. 1.Current status in health decision making quality of patient care...

  3. Current status in health decision making A day in the medical consultation • Healthy 45 year old woman • Infection of the urinary tract • Quinolonestherapy (16 in 4 gen) But whichoneshould be used? Gupta K, Hooton T, Naber K, Wullt B, Colgan R, Miller R, et al. International Clinical Practice Guidelines for the treatment of acute uncomplicated cystitis and pyelonephritis in women: A 2010 update by the InfectiousDiseasesSociety of America and the EuropeanSociety for Microbiology and InfectiousDiseases. Clin InfectDis 2011;52(5):e103-3120

  4. Current status in health decision making Dawes M, Sampson U; Knowledgemanagement in clinical practice: a systematic review of informationseekingbehavior in physicians. International Journal of Medical Informatics 2003; 71 :9 - 15

  5. Current status in health decision making Difficulties: • Decisions based on experience • Knowledge updated • Quality of the evidence • Analysis of the findings • Patient preferences Higgins JPT, Green S (editors). Cochrane Handbook for Systematic Reviews of Interventions V 5.1.0. The Cochrane Collaboration, 2011. www.Cochrane-handbook.org

  6. Current status in health decision making Difficulties: • Decisions based on experience

  7. Current status in health decision making Difficulties: • Knowledge updated • White/gray evidence • Central 14983 trials (2014) 41 per day

  8. Current status in health decision making Difficulties: • Quality of the evidence biasno bias

  9. Current status in health decision making Difficulties: • Analysis of the findings • Conventional meta-analysis (1 vs 1) • Multiplestrata • Meta-analysis for eachoutcome • No ranking ZalmanaviciTrestioreanu A, Green H, Paul M, Yaphe J, Leibovici L. Antimicrobial agents for treating uncomplicated urinary tract infection in women. Cochrane Database of Systematic Reviews 2010;10:CD007182

  10. There is a method called network meta-analysis, which allows to compare multiple interventions in order to rank them and facilitate decision-making.

  11. 2.Meta-analysis of multiple comparisons

  12. Network meta-analysis • compare multipleinterventions • accuracy and consistency of comparisons • analyze by metaregressions • hierarchize Interventions Catalá-López F, Tobias A, Roqué M. Basic concepts for network meta-analysis. Atención Primaria 2014;46(10):573-581

  13. Network meta-analysis Steps: • Direct comparisons • Heterogeneity (< 30%) • Indirect comparisons • Transitivity • Mixed comparisons • Consistency • Surface Under the Cumulative Ranking curve Salanati G, Ades A, Lonnidis JP. Graphical Methods and numericalsummaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. J Clin Epidemiol 2011;64:163-171

  14. Network meta-analysis Methods: • We performed a systematic review • 357 potential trials • 25 trials were included (11,400 participants) • 10 trials (3 arms) • 10 treatment schemes with quinolone

  15. Network meta-analysis Direct and indirect comparisons: • RR and 95% CI (randomeffects,varianceinverse, heterogeneity) • Variances and covariances • Commoncomparator (TMP/SMX) network setup d n, studyvar(study) rr trtvar(tx) sdpool(on) armvars(keep) ref(“TMP/SMX (160/800)”) • Graph of net network map, circle Catalá-López F, Tobias A, Roqué M. Basic concepts for network meta-analysis. Atención Primaria 2014;46(10):573-581

  16. Network meta-analysis Direct and indirect comparisons and transitivity:

  17. Network meta-analysis Consistency for each loop: • Univariate metaregression wiht dummys model Direct comparisons • Basic parameters yB-A = µ1 (Bucher´s method) yC-A = µ2 Indirect comparisons • Functional parameters yC-B = µ1-µ2 network sidesplit all Lu G, Ades AE. Assessing evidence inconsistency in mixed treatment comparisons. J Am Stat Assoc 2006;101(474):447-459

  18. Network meta-analysis Consistency for each loop:

  19. Network meta-analysis Consistency of net: • multivariate metagression • chi2 network meta inconsistency o mvmeta _y _S , bscovariance(exch 0.5) longparm suppress(uv mm) eq(_y_H: des_HIK) vars(_y_name of each intervention) Lu G, Ades AE. Assessing evidence inconsistency in mixed treatment comparisons. J Am Stat Assoc 2006;101(474):447-459

  20. Network meta-analysis Consistency of net:

  21. Network meta-analysis Graph of consistency for each loop: intervalplot, eform separate labels( name of interventions) reference(”TMP/SMX”) null(1)

  22. Network meta-analysis Surface Under the Cumulative Ranking curve: • Probabilities from metaregressions yAi=a + bx + e network rank max, all zero gen(prob) • SUCRA command sucraprob*, labels(name of each intervention) Chaimani A, Higgins J, Mavridis D, Spyridonos P, Salanti G. Graphical Tools for Network Meta-Analysis in STATA. PLoS One 2013;8(10):e76654

  23. Network meta-analysis Surface Under the Cumulative Ranking curve:

  24. Network meta-analysis Ranking of quinolones for UTI: Chaimani A, Higgins J, Mavridis D, Spyridonos P, Salanti G. Graphical Tools for Network Meta-Analysis in STATA. PLoS One 2013;8(10):e76654

  25. Network meta-analysis Clusterank between 2 outcomes: • Compare SUCRA between 2 outcomes clusterank outcome1 outcome2 treatment

  26. Network meta-analysis It facilitates decision making but requires transitivity and consistency to make its findings reliable.

  27. Thanks! Any questions? You can find me at: pegasso.100@hotmail.com Alejandro G. Gonzalez Garay, M.D., Ms.C, Ph.D. National Institute of Pediatrics

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