Sensitivity of treatment recommendations to bias in network meta-analysis

Journal of the Royal Statistical Society. Series A: Statistics in Society

Volumn 181, Issue 3, 2018, Pages 843-867

  Phillippo, David M ^a   Dias, Sofia ^a   Ades, A E ^a   Didelez, Vanessa ^b   Welton, Nicky J ^a  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

^b LEIBNIZ INSTITUTE FOR PREVENTION RESEARCH AND EPIDEMIOLOGY BIPS   (Germany)

Author keywords

Evidence synthesis; Influence matrix; Mixed treatment comparison; Quality of evidence; Risk of bias; Threshold analysis

Indexed keywords

EID: 85047861990     PISSN: 09641998     EISSN: 1467985X     Source Type: Journal    
DOI: 10.1111/rssa.12341     Document Type: Article

References (45)

1. Bernstein, D. S. (2005) Matrix Mathematics: Theory, Facts, and Formulas with Application to Linear Systems Theory. Princeton: Princeton University Press.
2. Boland, A., Dundar, Y., Bagust, A., Haycox, A., Hill, R. Mujica Mota, R., Walley, T. and Dickson, R. (2003) Early thrombolysis for the treatment of acute myocardial infarction: a systematic review and economic evaluation. Hlth Technol. Assessmnt, 7, 1–136.
3. Caldwell, D. M., Ades, A. E., Dias, S., Watkins, S., Li, T., Taske, N., Naidoo, B. and Welton, N. J. (2016) A threshold analysis assessed the credibility of conclusions from network meta-analysis. J. Clin. Epidem., 7, 68–76.
4. Caldwell, D. M., Ades, A. E. and Higgins, J. P. T. (2005) Simultaneous comparison of multiple treatments: combining direct and indirect evidence. Br. Med. J., 331, 897–900.
5. Cohen, J. (1988) Statistical Power Analysis for the Behavioral-sciences, 2nd edn. Erlbaum.
6. Critchfield, G. C. and Willard, K. E. (1986) Probabilistic analysis of decision trees using Monte Carlo simulation. Med. Decsn Makng, 6, 85–92.
7. Dias, S., Sutton, A. J., Ades, A. E. and Welton, N. J. (2013a) Evidence synthesis for decision making 2: A generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials. Med. Decsn Makng, 33, 607–617.
8. Dias, S., Sutton, A. J., Welton, N. J. and Ades, A. E. (2013b) Evidence synthesis for decision making 6: Embedding evidence synthesis in probabilistic cost-effectiveness analysis. Med. Decsn Makng, 33, 671–678.
9. Dias, S., Sutton, A. J., Welton, N. J. and Ades, A. E. (2013c) Evidence synthesis for decision making 3: Heterogeneity—subgroups, meta-regression, bias, and bias-adjustment. Med. Decsn Makng, 33, 618–640.
10. Dias, S., Welton, N. J., Marinho, V. C. C., Salanti, G., Higgins, J. P. T. and Ades, A. E. (2010) Estimation and adjustment of bias in randomized evidence by using mixed treatment comparison meta-analysis. J. R. Statist. Soc. A, 173, 613–629.
11. Dias, S., Welton, N. J., Sutton, A. J., Caldwell, D. M., Lu, G. B. and Ades, A. E. (2013) Evidence synthesis for decision making 4: inconsistency in networks of evidence based on randomized controlled trials. Med. Decsn Makng, 33, 641–656.
12. Dominici, F., Parmigiani, G., Wolpert, R. L. and Hasselblad, V. (1999) Meta-analysis of migraine headache treatments: combining information from heterogeneous designs. J. Am. Statist. Ass., 94, 16–28.
13. Doubilet, P., Begg, C. B., Weinstein, M. C., Braun, P. and McNeill, B. J. (1985) Probabilistic sensitivity analysis using Monte Carlo simulation: a practical approach. Med. Decsn Makng, 5, 157–177.
14. Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. and Rubin, D. B. (2013) Bayesian Data Analysis, 3rd edn. Boca Raton: Chapman and Hall–CRC.
15. Guyatt, G. H., Oxman, A. D., Vist, G., Kunz, R., Brozek, J., Alonso-Coello, P., Montori, V., Akl, E. A., Djulbegovic, D., Falck-Ytter, Y., Norris, S. L., Williams, J. W., Atkins, D., Meerpohl, J. and Schunemann, H. J. (2011) GRADE guidelines: 4, Rating the quality of evidence-study limitations (risk of bias). J. Clin. Epidem., 64, 407–415.
16. Higgins, J. P. T., Altman, D. G., Gøtzsche, P. C., Jüni, P., Moher, D., Oxman, A. D., Savović, J., Schulz, K. F., Weeks, L., Sterne, J. A. C., Cochrane Bias Methods Group and Cochrane Statistical Methods Group (2011) The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. Br. Med. J., 343, article d5928.
17. Higgins, J. P. T. and Whitehead, A. (1996) Borrowing strength from external trials in a meta-analysis. Statist. Med., 15, 2733–2749.
18. Kass, R. E. and Raftery, A. E. (1995) Bayes Factors. J. Am. Statist. Ass., 90, 773–795.
19. Keeley, E. C., Boura, J. A. and Grines, C. L. (2003) Primary angioplasty versus intravenous thrombolytic therapy for acute myocardial infarction: a quantitative review of 23 randomised trials. Lancet, 361, 13–20.
20. Konig, J., Krahn, U. and Binder, H. (2013) Visualizing the flow of evidence in network meta-analysis and characterizing mixed treatment comparisons. Statist. Med., 32, 5414–5429.
21. Krahn, U., Binder, H. and Konig, J. (2013) A graphical tool for locating inconsistency in network meta-analyses. BMC Med. Res. Methodol., 13, article 35.
22. Kullback, S. and Leibler, R. A. (1951) On information and sufficiency. Ann. Math. Statist., 22, 79–86.
23. Lawson, C. and Hanson, R. (1995) Solving Least Squares Problems. Philadelphia: Society for Industrial and Applied Mathematics.
24. Lu, G. and Ades, A. (2004) Combination of direct and indirect evidence in mixed treatment comparisons. Statist. Med., 23, 3105–3124.
25. Lu, G. and Ades, A. (2006) Assessing evidence consistency in mixed treatment comparisons. J. Am. Statist. Ass., 101, 447–459.
26. Lu, G., Welton, N. J., Higgins, J. P., White, I. R. and Ades, A. E. (2011) Linear inference for mixed treatment comparison meta-analysis: a two-stage approach. Res. Synth. Meth., 2, 43–60.
27. Lumley, T. (2002) Network meta-analysis for indirect treatment comparisons. Statist. Med., 21, 2313–2324.
28. Lunn, D. J., Thomas, A., Best, N. and Spiegelhalter, D. (2000) WinBUGS—a Bayesian modelling framework: concepts, structure, and extensibility. Statist. Comput., 10, 325–337.
29. Mayo-Wilson, E., Dias, S., Mavranezouli, I., Kew, K., Clark, D. M., Ades, A. E. and Pilling, S. (2014) Psychological and pharmacological interventions for social anxiety disorder in adults: a systematic review and network meta-analysis. Lancet Psychiat., 1, 368–376.
30. Naci, H., Dias, S. and Ades, A. E. (2014) Industry sponsorship bias in research findings: a network meta-analysis of LDL cholesterol reduction in randomised trials of statins. Br. Med. J., 349, article g7451.
31. National Collaborating Centre for Mental Health (2013) Social Anxiety Disorder: Recognition, Assessment and Treatment. Leicester and London: British Psychological Society and Royal College of Psychiatrists.
32. National Institute for Health and Care Excellence (2013) Guide to the Methods of Technology Appraisal 2013. London: National Institute for Health and Care Excellence.
33. National Institute for Health and Care Excellence (2014) Developing NICE Guidelines: the Manual. London: National Institute for Health and Care Excellence.
34. Puhan, M. A., Schunemann, H. J., Murad, M. H., Li, T., Brignardello-Petersen, R., Singh, J. A., Kessels, A. G., Guyatt, G. H. and GRADE Working Group (2014) A GRADE Working Group approach for rating the quality of treatment effect estimates from network meta-analysis. Br. Med. J., 349,.
35. Rhodes, K. M., Turner, R. M. and Higgins, J. P. T. (2015) Predictive distributions were developed for the extent of heterogeneity in meta-analyses of continuous outcome data. J. Clin. Epidem., 68, 52–60.
36. Rothwell, P. M. (2005) External validity of randomised controlled trials: “To whom do the results of this trial apply?”. Lancet, 365, 82–93.
37. Salanti, G., Del Giovane, C., Chaimani, A., Caldwell, D. M. and Higgins, J. P. (2014) Evaluating the quality of evidence from a network meta-analysis. PLOS One, 9,.
38. Salanti, G., Dias, S., Welton, N. J., Ades, A. E., Golfinopoulos, V., Kyrgiou, M., Mauri, D. and Ioannidis, J. P. A. (2010) Evaluating novel agent effects in multiple-treatments meta-regression. Statist. Med., 29, 2369–2383.
39. Savović, J., Jones, H. E., Altman, D. G., Harris, R. J., Jüni, P., Pildal, J., Als-Nielsen, B., Balk, E. M., Gluud, C., Gluud, L. L., Ioannidis, J. P. A., Schulz, K. F., Beynon, R., Welton, N. J., Wood, L., Moher, D., Deeks, J. J. and Sterne, J. A. C. (2012a) Influence of reported study design characteristics on intervention effect estimates from randomised controlled trials: combined analysis of meta-epidemiological studies. Hlth Technol. Assessmnt, 16, 1–102.
40. Savović, J., Jones, H. E., Altman, D. G., Harris, R. J., Jüni, P., Pildal, J., Als-Nielsen, B., Balk, E. M., Gluud, C., Gluud, L. L., Ioannidis, J. P. A., Schulz, K. F., Beynon, R., Welton, N. J., Wood, L., Moher, D., Deeks, J. J. and Sterne, J. A. C. (2012b) Influence of reported study design characteristics on intervention effect estimates from randomized, controlled trials. Ann. Intern. Med., 157, 429–438.
41. Schulz, K. F., Chalmers, I., Hayes, R. J. and Altman, D. G. (1995) Empirical-evidence of bias—dimensions of methodological quality associated with estimates of treatment effects in controlled trials. J. Am. Med. Ass., 273, 408–412.
42. Stinnett, A. and Mullahy, J. (1998) Net health benefits: a new framework for the analysis of uncertainty in cost-effectiveness analyses. Med. Decsn Makng, 18, S68–S80.
43. Turner, R. M., Jackson, D., Wei, Y., Thompson, S. G. and Higgins, J. P. T. (2015) Predictive distributions for between-study heterogeneity and simple methods for their application in Bayesian meta-analysis. Statist. Med., 34, 984–998.
44. Turner, R. M., Spiegelhalter, D. J., Smith, G. C. S. and Thompson, S. G. (2009) Bias modelling in evidence synthesis. J. R. Statist. Soc. A, 172, 21–47.
45. Welton, N. J., Ades, A. E., Carlin, J. B., Altman, D. G. and Sterne J. A. C. (2009) Models for potentially biased evidence in meta-analysis using empirically based priors. J. R. Statist. Soc. A, 172, 119–136.