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 8/2013

01-08-2013 | Original Research Article

A Choice That Matters?

Simulation Study on the Impact of Direct Meta-Analysis Methods on Health Economic Outcomes

Authors: Pepijn Vemer, Maiwenn J. Al, Mark Oppe, Maureen P. M. H. Rutten-van Mölken

Published in: PharmacoEconomics | Issue 8/2013

Login to get access

Abstract

Background

Decision-analytic cost-effectiveness (CE) models combine many different parameters like transition probabilities, event probabilities, utilities and costs, which are often obtained after meta-analysis. The method of meta-analysis may affect the CE estimate.

Aim

Our aim was to perform a simulation study that compares the performance of different methods of meta-analysis, especially with respect to model-based health economic (HE) outcomes.

Methods

A reference patient population of 50,000 was simulated from which sets of samples were drawn. Each sample drawn represented a clinical trial comparing two fictitious interventions. In several scenarios, the heterogeneity between these trials was varied, by drawing one or more of the trials from predefined subpopulations. Parameter estimates from these trials were combined using frequentist fixed (FFE) and random effects (FRE), and Bayesian fixed (BFE) and random effects (BRE) meta-analysis. The pooled parameter estimates were entered into a probabilistic cost-effectiveness Markov model. The four methods of meta-analysis resulted in different parameter estimates and HE outcomes, which were compared with the true values in the reference population. Performance statistics were: (1) the percentage of repetitions that the confidence interval of the probabilistic sensitivity analysis covers the true value (coverage), (2) the difference between the estimated and true value (bias), (3) the mean absolute value of the bias (MAD) and (4) the percentage of repetitions that result in a statistically significant difference between the two interventions (statistical power). As the differences between methods could be due to chance, we repeated every step of the analysis 1,000 times to study whether differences were systematic.

Results

FFE, FRE and BFE lead to different parameter estimates, but, when entered into the model, they do not lead to large differences in the point estimates of the HE outcomes, even in scenarios where we built in heterogeneity. Random effects methods do not necessarily reduce bias when heterogeneity is added to the trials, and may even increase bias in certain situations. BRE tends to overestimate uncertainty reflected in the CE acceptability curve.

Conclusion

FFE, FRE and BFE lead to comparable HE outcomes. BRE tends to overestimate uncertainty. Based on this study, we recommend FRE as the preferred method of meta-analysis.
Appendix
Available only for authorised users
Literature
1.
2.
Cooper NJ, Sutton AJ, Abrams KR, Turner D, Wailoo A. Comprehensive decision analytical modelling in economic evaluation: a Bayesian approach. Health Econ. 2004;13(3):203–26.CrossRefPubMed
3.
Ades AE, Sculpher M, Sutton A, Abrams K, Cooper N, Welton N, et al. Bayesian methods for evidence synthesis in cost-effectiveness analysis. Pharmacoeconomics. 2006;24(1):1–19.CrossRefPubMed
4.
Riley RD, Simmonds MC, Look MP. Evidence synthesis combining individual patient data and aggregate data: a systematic review identified current practice and possible methods. J Clin Epidemiol. 2007;60(5):431–9.CrossRefPubMed
5.
Brockwell SE, Gordon IR. A comparison of statistical methods for meta-analysis. Stat Med. 2001;20(6):825–40.
6.
Sutton AJ, Abrams KR. Bayesian methods in meta-analysis and evidence synthesis. 2001;10(4):277–303.
7.
Sutton AJ, Higgins JP. Recent developments in meta-analysis. Stat Med. 2008;27(5):625–50.
8.
9.
Oppe M, Al M, Rutten-van Molken M. Comparing methods of data synthesis: re-estimating parameters of an existing probabilistic cost-effectiveness model. Pharmacoeconomics. 2011;29(3):239–50.CrossRefPubMed
10.
Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557–60.CrossRefPubMed
11.
DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–88.
12.
Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–58.
13.
Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian data analysis. London: Chapman & Hall; 1995.
14.
Carlin BP, Louis TA. Bayes and empirical Bayes methods for data analysis. London: Chapman & Hall; 1996.
15.
Jaynes E. Confidence intervals vs Bayesian intervals. In: Harper W, Hooker CA, editors. Foundations of probability theory, statistical inference, and statistical theories of science. Dordrecht: D Reidel; 1976. p. 175.CrossRef
16.
O’Hagan A, Luce B. A primer on Bayesian statistics in health economics and outcomes research. Sheffield: Centre for Bayesian Statistics in Health Economics; 2003.
17.
Lambert PC, Sutton AJ, Burton PR, Abrams KR, Jones DR. How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS. Stat Med. 2005;24(15):2401–28.CrossRefPubMed
18.
Song F, Altman DG, Glenny AM, Deeks JJ. Validity of indirect comparison for estimating efficacy of competing interventions: empirical evidence from published meta-analyses. BMJ. 2003;326(7387):472.
19.
Strassmann R, Bausch B, Spaar A, Kleijnen J, Braendli O, Puhan MA. Smoking cessation interventions in COPD: a network meta-analysis of randomised trials. Eur Respir J. 2009;34(3):634–40.CrossRefPubMed
20.
Puhan MA, Bachmann LM, Kleijnen J, Ter Riet G, Kessels AG. Inhaled drugs to reduce exacerbations in patients with chronic obstructive pulmonary disease: a network meta-analysis. BMC Med. 2009;14(7):2.CrossRef
21.
Lu G, Ades AE. Assessing evidence inconsistency in mixed treatment comparisons. J Am Stat Assoc. 2006;101:447–59.CrossRef
Metadata
Title
A Choice That Matters?
Simulation Study on the Impact of Direct Meta-Analysis Methods on Health Economic Outcomes
Authors
Pepijn Vemer
Maiwenn J. Al
Mark Oppe
Maureen P. M. H. Rutten-van Mölken
Publication date
01-08-2013
Publisher
Springer International Publishing
Published in
PharmacoEconomics / Issue 8/2013
Print ISSN: 1170-7690
Electronic ISSN: 1179-2027
DOI
https://doi.org/10.1007/s40273-013-0067-0

Other articles of this Issue 8/2013

PharmacoEconomics 8/2013 Go to the issue

Review Article

Overview of Parametric Survival Analysis for Health-Economic Applications

Review Article

Golimumab for the Treatment of Rheumatoid Arthritis After the Failure of Previous Disease-Modifying Antirheumatic Drugs: A NICE Single Technology Appraisal

Practical Application

Good Practice Guidelines for the use of Statistical Regression Models in Economic Evaluations

Original Research Article

Understanding the Cost-Effectiveness of Influenza Vaccination in Children: Methodological Choices and Seasonal Variability

Leading Article

Constructing Indirect Utility Models: Some Observations on the Principles and Practice of Mapping to Obtain Health State Utilities

Original Research Article

Real-World Cost-Effectiveness of Oxaliplatin in Stage III Colon Cancer: A Synthesis of Clinical Trial and Daily Practice Evidence