Evidence synthesis for decision making 2: A generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials

Medical Decision Making

Volumn 33, Issue 5, 2013, Pages 607-617

  Dias, Sofia ^a   Sutton, Alex J ^b   Ades, A E ^b   Welton, Nicky J ^b  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

^b NONE

Author keywords

generalized linear model; indirect evidence; meta analysis; network meta analysis

Indexed keywords

ARTICLE; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; DECISION MAKING; EVIDENCE BASED MEDICINE; GENERALIZED LINEAR MODEL; INDIRECT EVIDENCE; META ANALYSIS; NETWORK META-ANALYSIS; RANDOMIZED CONTROLLED TRIAL; STATISTICAL MODEL; THEORETICAL MODEL;

GENERALIZED LINEAR MODEL; INDIRECT EVIDENCE; META-ANALYSIS; NETWORK META-ANALYSIS;

DECISION MAKING; EVIDENCE-BASED MEDICINE; LIKELIHOOD FUNCTIONS; MODELS, THEORETICAL;

EID: 84879740382     PISSN: 0272989X     EISSN: 1552681X     Source Type: Journal    
DOI: 10.1177/0272989X12458724     Document Type: Article

References (61)

1. Sutton AJ, Higgins JPT. Recent developments in meta-analysis. Stat Med. 2008 ; 27: 625-50
2. Whitehead A Meta-analysis of Controlled Clinical Trials. Chichester, UK: Wiley ; 2002:
3. Whitehead A, Whitehead J. A general parameteric approach to the meta-analysis of randomised clinical trials. Stat Med. 1991 ; 10: 1665-77
4. Zelen M. The analysis of several 2 × 2 tables. Biometrika. 1971 ; 58: 129-37
5. DerSimonian R, Laird N. Meta-analysis of clinical trials. Control Clin Trials. 1986 ; 7: 177-88
6. Cooper H, Hedges L The Handbook of Research Synthesis. New York: Russell Sage Foundation ; 1994:
7. Egger M, Davey-Smith G, Altman D Systematic Reviews in Health Care: Meta-analysis in Context. 2 nd ed.London: BMJ; 2001. London: BMJ ; 2001:
8. Higgins J Green S, ed. Chichester, UK: Cochrane Collaboration ; 2008:
9. Smith TC, Spiegelhalter DJ, Thomas A. Bayesian approaches to random-effects meta-analysis: a comparative study. Stat Med. 1995 ; 14: 2685-99
10. Glenny AM, Altman DG, Song F, et al. Indirect comparisons of competing interventions. Health Technol Assess. 2005 ; 9 (26). 1-134
11. Ades AE. A chain of evidence with mixed comparisons: models for multi-parameter evidence synthesis and consistency of evidence. Stat Med. 2003 ; 22: 2995-3016
12. Caldwell DM, Ades AE, Higgins JPT. Simultaneous comparison of multiple treatments: combining direct and indirect evidence. BMJ. 2005 ; 331: 897-900
13. Welton NJ, Ades AE. A model of toxoplasmosis incidence in the UK: evidence synthesis and consistency of evidence. Appl Stat. 2005 ; 54: 385-404
14. Gleser LJ, Olkin I The Handbook of Research Synthesis. Cooper H Hedges LV, ed. New York: Russell Sage Foundation ; 1994: 339-55.
15. Gleser L, Olkin I Meta-analysis in Medicine and Health Policy. Stangl DK Berry DA, ed. New York: Marcel Dekker ; 2000:
16. Hasselblad V. Meta-analysis of multi-treatment studies. Med Decis Making. 1998 ; 18: 37-43
17. Higgins JPT, Whitehead A. Borrowing strength from external trials in a meta-analysis. Stat Med. 1996 ; 15: 2733-49
18. Lu G, Ades A. Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med. 2004 ; 23: 3105-24
19. Lu G, Ades A. Assessing evidence consistency in mixed treatment comparisons. J Am Stat Assoc. 2006 ; 101: 447-59
20. Lumley T. Network meta-analysis for indirect treatment comparisons. Stat Med. 2002 ; 21: 2313-24
21. Salanti G, Higgins JPT, Ades AE, Ioannidis JPA. Evaluation of networks of randomised trials. Stat Methods Med Res. 2008 ; 17: 279-301
22. Guide to the Methods of Technology Appraisal. London: NICE ; 2008:
23. McCullagh P, Nelder JA Generalised Linear Models. 2 nd ed.London: Chapman & Hall; 1989. London: Chapman & Hall ; 1989:
24. Lunn DJ, Thomas A, Best N, Spiegelhalter D. WinBUGS - a Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput. 2000 ; 10: 325-37
25. DiasSWeltonNJSuttonAJAdesAE. NICE DSU technical support document 2: a generalised linear modelling framework for pair-wise and network meta-analysis of randomised controlled trials. Available from: http://www.nicedsu.org.uk
26. Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesian measures of model complexity and fit. J Roy Stat Soc B. 2002 ; 64 (4). 583-616
27. Bernardo JM, Smith AFM Bayesian Theory. New York: Wiley ; 1994:
28. SpiegelhalterDThomasABestNLunnD. WinBUGS user manual. Version 1.4, January 2003. Version 1.4.3, 2007. http://wwwmrc-bsucamacuk/bugs.
29. Carlin JB. Meta-analysis for 2 × 2 tables: a Bayesian approach. Stat Med. 1992 ; 11: 141-58
30. van Houwelingen HC, Zwinderman KH, Stijnen T. A bivariate approach to meta-analysis. Stat Med. 1993 ; 12: 2273-84
31. van Houwelingen HC, Arends LR, Stijnen T. Advanced methods in meta-analysis: multi-variate approach and meta-regression. Stat Med. 2002 ; 21: 589-624
32. Dias S, Welton NJ, Sutton AJ, Ades AE. Evidence synthesis for decision making 5: the baseline natural history model. Med Decis Making. 2013 ; 33 (5). 657-670
33. Dempster AP. The direct use of likelihood for significance testing. Stat Comput. 1997 ; 7: 247-52
34. SpiegelhalterD. Two brief topics on modelling with WinBUGS. IceBugs: a workshop about the development and use of the BUGS programme. Available from: http://www.math.helsinki.fi/openbugs/IceBUGS/Presentations/SpiegelhalterIceBUGS. pdf
35. Dias S, Welton NJ, Sutton AJ, Caldwell DM, Lu G, Ades AE. Evidence synthesis for decision making 4: inconsistency in networks of evidence based on randomized controlled trials. Med Decis Making. 2013 ; 33 (5). 641-656
36. Deeks JJ. Issues on the selection of a summary statistic for meta-analysis of clinical trials with binary outcomes. Stat Med. 2002 ; 21: 1575-600
37. Caldwell DM, Welton NJ, Dias S, Ades AE. Selecting the best scale for measuring treatment effect in a network meta-analysis: a case study in childhood nocturnal enuresis. Res Syn Meth. 2012 ; 3: 111-25
38. Cooper NJ, Sutton AJ, Lu G, Khunti K. Mixed comparison of stroke prevention treatments in individuals with nonrheumatic atrial fibrillation. Arch Intern Med. 2006 ; 166: 1269-75
39. Dias S, Welton NJ, Marinho VCC, Salanti G, Higgins JPT, Ades AE. Estimation and adjustment of bias in randomised evidence by using mixed treatment comparison meta-analysis. J Roy Stat Soc A. 2010 ; 173: 613-29
40. Guevara JP, Berlin JA, Wolf FM. Meta-analytic methods for pooling rates when follow-up duration varies: a case study. BMC Med Res Methodol. 2004 ; 4: 17-23
41. Collett D London: Chapman & Hall ; 1994:
42. London: NICE ; 2010:
43. Ades AE, Mavranezouli I, Dias S, Welton NJ, Whittington C, Kendall T. Network meta-analysis with competing risk outcomes. Value Health. 2010 ; 13 (8). 976-83
44. Frison L, Pocock SJ. Repeated measures in clinical trials: analysis using mean summary statistics and its implications for design. Stat Med. 1992 ; 11: 1685-704
45. Follmann D, Elliott P, Suh I, Cutler J. Variance imputation for overviews of clinical trials with continuous response. J Clin Epidemiol. 1992 ; 45: 769-73
46. Abrams KR, Gillies CL, Lambert PC. Meta-analysis of heterogeneously reported trials assessing change from baseline. Stat Med. 2005 ; 24: 3823-44
47. Cohen J New York: Academic Press ; 1969:
48. Hedges LV, Olkin I London: Academic Press ; 1985:
49. Rothman KJ, Greenland S, Lash TL Modern Epidemiology. 3 rd ed.Philadelphia: Lippincott, Williams & Wilkins; 2008. Philadelphia: Lippincott, Williams & Wilkins ; 2008:
50. Warn DE, Thompson SG, Spiegelhalter DJ. Bayesian random effects meta-analysis of trials with binary outcomes: method for absolute risk difference and relative risk scales. Stat Med. 2002 ; 21: 1601-23
51. Lu G, Ades AE. Modelling between-trial variance structure in mixed treatment comparisons. Biostatistics. 2009 ; 10: 792-805
52. Raiffa H, Schlaiffer R New York: Wiley Interscience ; 1967:
53. Franchini A, Dias S, Ades AE, Jansen J, Welton N. Accounting for correlation in mixed treatment comparisons with multi-arm trials. Res Syn Meth. 2012 ; 3: 142-60
54. Parmigiani G Modeling in Medical Decision Making: a Bayesian Approach. San Francisco: Wiley ; 2002:
55. Claxton K, Sculpher M, McCabe C, et al. Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra. Health Econ. 2005 ; 14: 339-47
56. Ades AE, Claxton K, Sculpher M. Evidence synthesis, parameter correlation and probabilistic sensitivity analysis. Health Econ. 2005 ; 14: 373-81
57. Briggs A. Handling uncertainty in economic evaluation. Br Med J. 1999 ; 319: 120
58. Turner RM, Davey J, Clarke MJ, Thompson SG, Higgins JPT. Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews. Int J Epidemiol. 2012 ; 41: 818-27
59. Hooper L, Summerbell CD, Higgins JPT, et al. Reduced or modified dietary fat for preventing cardiovascular disease. Cochrane Database Syst Rev. 2000 ; 2:
60. Elliott WJ, Meyer PM. Incident diabetes in clinical trials of antihypertensive drugs: a network meta-analysis. Lancet. 2007 ; 369: 201-7
61. Woolacott N, Hawkins NS, Mason A, et al. Etanercept and efalizumab for the treatment of psoriasis: a systematic review. Health Technol Assess. 2006 ; 10 (46). 1-233