Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
PharmacoEconomics
Published in: PharmacoEconomics 9/2008

01-09-2008 | Briefing Paper

Use of Indirect and Mixed Treatment Comparisons for Technology Assessment

Authors: Dr Alex Sutton, A. E. Ades, Nicola Cooper, Keith Abrams

Published in: PharmacoEconomics | Issue 9/2008

Login to get access

Abstract

Indirect and mixed treatment comparison (MTC) approaches to synthesis are logical extensions of more established meta-analysis methods. They have great potential for estimating the comparative effectiveness of multiple treatments using an evidence base of trials that individually do not compare all treatment options. Connected networks of evidence can be synthesized simultaneously to provide estimates of the comparative effectiveness of all included treatments and a ranking of their effectiveness with associated probability statements.
The potential of the use of indirect and MTC methods in technology assessment is considerable, and would allow for a more consistent assessment than simpler alternative approaches. Although such models can be viewed as a logical and coherent extension of standard pair-wise meta-analysis, their increased complexity raises some unique issues with far-reaching implications concerning how we use data in technology assessment, while simultaneously raising searching questions about standard pair-wise meta-analysis. This article reviews pair-wise meta-analysis and indirect and MTC approaches to synthesis, clearly outlining the assumptions involved in each approach. It also raises the issues that the National Institute for Health and Clinical Excellence (NICE) needed to consider in updating their 2004 Guide to the Methods of Technology Appraisal, if such methods are to be used in their technology appraisals.
Literature
1.
Hierarchy of evidence and grading of recommendations. Thorax 2004; 59 (Suppl. 1): 13
2.
Egger M, Davey Smith G, Altman DG. Systematic reviews in health care: meta-analysis in context. London: BMJ Books, 2000
3.
Higgins JPT, Green S, editors. Cochrane handbook for systematic reviews of interventions, 4.2.5 [updated May 2005]. In: The Cochrane Library. Issue 3. Chichester: John Wiley & Sons, Ltd, 2005
4.
Higgins JPT, Whitehead A. Borrowing strength from external trials in a meta-analysis. Stat Med 1996; 15: 2733–2749PubMedCrossRef
5.
6.
7.
Lu G, Ades AE. Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med 2004; 23: 3105–3124PubMedCrossRef
8.
Caldwell DM, Ades AE, Higgins JPT. Simultaneous comparison of multiple treatments: combining direct and indirect evidence. BMJ 2005; 331: 897–900PubMedCrossRef
9.
Glenny AM, Altman DG, Song F. Indirect comparisons of competing interventions. Health Technol Assess 2005; 9 (26): 1–148PubMed
10.
National Institute for Health and Clinical Excellence (NICE). Guide to the methods of technology appraisal. London: NICE 2004
11.
Salanti G, Higgins JPT, Ades AE, et al. Evaluation of networks of randomized trials. Stat Methods Med Res 2007; 17: 279–301PubMedCrossRef
12.
Sutton AJ, Abrams KR, Jones DR, et al. Methods for meta-analysis in medical research. London: John Wiley, 2000
13.
Furberg CT, Morgan TM. Lessons from overviews of cardiovascular trials. Stat Med 1987; 6: 295–303PubMedCrossRef
14.
Gillies CL, Abrams KR, Lambert PC, et al. Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis. BMJ 2007; 334: 299–302PubMedCrossRef
15.
Stewart LA, Clarke MJ. Practical methodology of meta-analyses (overviews) using updated individual patient data. Cochrane Working Group. Stat Med 1995; 14: 2057–2079PubMedCrossRef
16.
Sculpher M. Subgroups and heterogeneity in cost-effectiveness analysis. Pharmacoeconomics 2008; 26 (9): 799–806PubMedCrossRef
17.
Deeks JJ. Issues in the selection of a summary statistic for meta-analysis of clinical trials with binary outcomes. Stat Med 2002; 21: 1575–1600PubMedCrossRef
18.
DerSimonian R, Kacker R. Random-effects model for meta-analysis of clinical trials: an update. Contemp Clin Trials 2007; 28: 105–114PubMedCrossRef
19.
Greenland S. Can meta-analysis be salvaged? Am J Epidemiol 1994; 140: 783–787PubMed
20.
Ades AE, Lu G, Higgins JPT. The interpretation of random-effects meta-analysis in decision models. Med Decis Making 2005; 25: 646–654PubMedCrossRef
21.
Welton NJ, White JR, Lu G, et al. Correction: interpretation of random effects meta-analysis in decision models. Med Decis Making 2007; 27: 212–214PubMedCrossRef
22.
Thompson SG, Sharp SJ. Explaining heterogeneity in meta-analysis: a comparison of methods. Stat Med 1999; 18: 2693–2708PubMedCrossRef
23.
Lambert P, Sutton AJ, Abrams KR, et al. A comparison of summary patient level covariates in meta-regression with individual patient data meta-analyses. J Clin Epidemiol 2002; 55: 86–94PubMedCrossRef
24.
Turner RM, Spiegelhalter DJ, Smith GCS, et al. Bias modelling in evidence synthesis. J R Stat Soc Ser A Stat Soc. In press
25.
Bucher HC, Guyatt GH, Griffith LE, et al. The results of direct and indirect treatment comparisons in meta-analysis of randomised controlled trials. J Clin Epidemiol 1997; 50: 683–691PubMedCrossRef
26.
Barrio V. Actual methodological controversies on the controlled clinical trials and on meta-analysis. Nefrologia 1998; 18: 32–39
27.
Song F, Harvey I, Lilford R. Adjusted indirect comparison may be less biased than direct comparison for evaluating new pharmaceutical interventions. J Clin Epidemiol 2008 May; 61 (5); 455–463PubMedCrossRef
28.
Lu G, Ades AE. Assessing evidence inconsistency in mixed treatment comparisons. J Am Stat Assoc 2006; 101: 447–459CrossRef
29.
Song F, Altman DG, Glenny M-A, et al. Validity of indirect comparison for estimating efficacy of competing interventions: empirical evidence from published meta-analyses. BMJ 2003; 326: 472–476PubMedCrossRef
30.
Spiegelhalter DJ, Thomas A, Best NG. WinBUGS version 1.2 user manual. Cambridge (UK): MRC Biostatistics Unit, 1999
31.
Ades AE, Sculpher M, Sutton A, et al. Bayesian methods for evidence synthesis in cost-effectiveness analysis. Pharmacoeconomics 2006; 24 (1): 1–19PubMedCrossRef
32.
Efron B, Tibshirani RJ. An introduction to the bootstrap. 1st ed. New York: Chapman & Hall, 1993
33.
Li ZH, Begg CB. Random effects models for combining results from controlled and uncontrolled studies in a metaanalysis. J Am Stat Assoc 1994; 89: 1523–1527CrossRef
34.
Lu G, Ades AE, Sutton AJ, et al. Meta-analysis of multiple treatment comparisons at multiple follow-up times. Stat Med 2007; 20: 3681–3699CrossRef
35.
Nixon RM, Bansback N, Brennan A. Using mixed treatment comparisons and meta-regression to perform indirect comparisons to estimate the efficacy of biologic treatments in rheumatoid arthritis. Stat Med 2007; 26: 1237–1254PubMedCrossRef
36.
Welton N, Cooper NJ, Ades A, et al. Mixed treatment comparison with multiple outcomes reported inconsistently across trials: evaluation of antivirals for treatment of influenza A and B. Stat Med. In press
37.
Moher D, Cook DJ, Eastwood S, et al. Improving the quality of reporting of meta-analysis of randomised controlled trials: the QUOROM statement. Lancet 1999; 354: 1896–1900PubMedCrossRef
38.
Cooper NJ, Sutton AJ, Lu G, et al. Mixed comparison of stroke prevention treatments in individuals with non-rheumatic atrial fibrillation. Arch Intern Med 2006; 166 (12): 1269–1275PubMedCrossRef
Metadata
Title
Use of Indirect and Mixed Treatment Comparisons for Technology Assessment
Authors
Dr Alex Sutton
A. E. Ades
Nicola Cooper
Keith Abrams
Publication date
01-09-2008
Publisher
Springer International Publishing
Published in
PharmacoEconomics / Issue 9/2008
Print ISSN: 1170-7690
Electronic ISSN: 1179-2027
DOI
https://doi.org/10.2165/00019053-200826090-00006

Other articles of this Issue 9/2008

PharmacoEconomics 9/2008 Go to the issue

Editorial

NICE Methodology for Technology Appraisals

Briefing Paper

Estimating ‘Costs’ for Cost-Effectiveness Analysis

Editorial

NICE’s 2008 Methods Guide

Briefing Paper

The NICE Cost-Effectiveness Threshold

Briefing Paper

Valuing Health States for Use in Cost-Effectiveness Analysis

Briefing Paper

Exploring Uncertainty in Cost-Effectiveness Analysis