Evidence synthesis assumes additivity on the scale of measurement: Response to rank reversal in indirect comparisons by norton et al.

Value in Health

Volumn 16, Issue 2, 2013, Pages 449-451

  Van Valkenhoef, Gert ^a   Ades, A E ^b  

^a UNIVERSITY MEDICAL CENTER GRONINGEN   (Netherlands)

^b UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ATTRIBUTABLE RISK; DECISION MAKING; HUMAN; LETTER; MEASUREMENT; PRIORITY JOURNAL;

EID: 84875437971     PISSN: 10983015     EISSN: 15244733     Source Type: Journal    
DOI: 10.1016/j.jval.2012.11.012     Document Type: Letter

References (19)

1. Norton EC, Miller MM, Wang JJ, et al. Rank reversal in indirect comparisons. Value Health 2012;volume:page range.
2. H.C. Bucher, G.H. Guyatt, L.E. Griffith, and S.D. Walter The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials J Clin Epidemiol 50 1997 683 691
3. F. Song, D.G. Altman, A.M. Glenny, and J.J. Deeks Validity of indirect comparison for estimating efficacy of competing interventions: empirical evidence from published meta-analyses BMJ 326 2003 472 476
4. L.V. Hedges, and I. Olkin Statistical Methods for Meta-Analysis 1985 Academic Press London
5. D.M. Caldwell, and A.E. Ades Higgins JPT. Simultaneous comparison of multiple treatments: combining direct and indirect evidence BMJ 331 2005 897 900
6. T. Lumley Network meta-analysis for indirect treatment comparisons Stat Med 21 2002 2313 2324
7. G. Lu, and A.E. Ades Combination of direct and indirect evidence in mixed treatment comparisons Stat Med 23 2004 3105 3124
8. G. Lu, and A.E. Ades Assessing evidence inconsistency in mixed treatment comparisons J Am Stat Assoc 101 2006 447 459
9. G. Lu, and A.E. Ades Modeling between-trial variance structure in mixed treatment comparisons Biostatistics 10 2009 792 805
10. R. DerSimonian, and N. Laird Meta-analysis in clinical trials Controlled Clin Trials 7 1986 177 188
11. G. Lu, N.J. Welton, and J.P.T. Higgins Linear inference for mixed treatment comparison meta-analysis: a two-stage approach Res Synth Methods 2 2011 43 60
12. D.M. Caldwell, N.J. Welton, S. Dias, and A.E. Ades Selecting the best scale for measuring treatment effect in a network meta-analysis: a case study in childhood nocturnal enuresis Res Synth Methods 3 2012 126 141
13. J.J. Deeks Issues in the selection of a summary statistic for meta-analysis of clinical trials with binary outcomes Stat Med 21 2002 1575 1600
14. Dias S, Welton NJ, Sutton AJ, Ades AE. NICE D.S.U. technical support document 3: Heterogeneity: subgroups, meta-regression, bias and bias-adjustment. Technical report, 2011. Available from: http://www.nicedsu.org.uk/. [Accessed October 18, 2012].
15. J.P. Jansen, R. Fleurence, and B. Devine 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 Value Health 14 2011 417 428
16. D.C. Hoaglin, N. Hawkins, and J.P. Jansen Conducting indirect-treatment- comparison and network-meta-analysis studies: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices Value Health 14 2011 429 437
17. F. Song, T. Xiong, and S. Parekh-Bhurke Inconsistency between direct and indirect comparisons of competing interventions: meta-epidemiological study BMJ 343 2011 d4909
18. T. Li, M.A. Puhan, and S.S. Vedula Network meta-analysis - highly attractive but more methodological research is needed BMC Med 9 2011 79
19. E.J. Mills, J.P. Ioannidis, and K. Thorlund How to use an article reporting a multiple treatment comparison meta-analysis JAMA 308 2012 1246 1253