Abstract
Background: Cost-effectiveness models should always be amendable to updating once new data on important model parameters become available. However, several methods of synthesizing data exist and the choice of method may affect the cost-effectiveness estimates.
Objectives: To investigate the impact of the different methods of metaanalysis on final estimates of cost effectiveness from a probabilistic Markov model for chronic obstructive pulmonary disease (COPD).
Methods: We compared four different methods to synthesize data for the parameters of a cost-effectiveness model for COPD: frequentist and Bayesian fixed-effects (FE) and random-effects (RE) meta-analyses. These methods were applied to obtain new transition probabilities between stable disease states and new event probabilities.
Results: The four methods resulted in different estimates of probabilities and their standard errors (SE). The effects of using different synthesis techniques were most prominent in the estimation of the SEs. We found up to 9-fold differences in SEs of the exacerbation probabilities and up to almost 3-fold differences in SEs of the transition probabilities. We found that the frequentist FE model produced the lowest SEs, whereas the Bayesian RE model produced the highest for all parameters. The estimates of differences between treatments in total costs, QALYs and cost-effectiveness acceptability curves (CEAC) also varied depending on the synthesis method. The CEAC was 15% lower with a Bayesian RE model than with any of the other models.
Conclusions: Health economic modellers should be aware that the choice of synthesis technique can affect resulting model parameters considerably, which can in turn affect estimates of cost effectiveness and the uncertainty around them.
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References
Global Initiative for Chronic Obstructive Lung Disease. Global strategy for diagnosis, management, and prevention of COPD. Bethesda (MD): National Institutes of Health, National Heart, Lung and Blood Institute, 2009 Dec [online]. Available from URL: http://www.goldcopd.com/Guidelineitem.asp?l1=2&l2=1&intId=2003 [Accessed 2006 Jul 6]
Rutten-van Mölken M, Lee TA. Economic modeling in chronic obstructive pulmonary disease. Proc Am Thorac Soc 2006 Sep; 3 (7): 630–4
Oostenbrink JB, Rutten-van Molken MP, Monz BU, et al. Probabilistic Markov model to assess the cost-effectiveness of bronchodilator therapy in COPD patients in different countries. Value Health 2005; 8: 32–46
Rutten-van Mölken MP, Oostenbrink JB, Miravitlles M, et al. Modelling the 5-year cost effectiveness of tiotropium, salmeterol and ipratropium for the treatment of chronic obstructive pulmonary disease in Spain. Eur J Health Econ 2007; 8: 123–35
Oostenbrink JB, Al MJ, Oppe M, et al. Expected value of perfect information: an empirical example of reducing decision uncertainty by conducting additional research. Value Health 2008; 11: 1070–80
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: 431–9
Der Simonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986; 7: 177–88
Sutton AJ, Abrams K, Jones DR, et al. Methods for meta-analysis in medical research. London: Wiley, 2000
Vincken W, van Noord JA, Greefhorst AP, et al. Improved health outcomes in patients with COPD during 1 year’s treatment with tiotropium. Eur Respir J 2002; 19: 209–16
Casaburi R, Mahler DA, Jones PW, et al. A long-term evaluation of once-daily inhaled tiotropium in chronic obstructive pulmonary disease. Eur Respir J 2002; 19: 217–24
Brusasco V, Hodder R, Miravitlles M, et al. Health outcomes following treatment for six months with once daily tiotropium compared with twice daily salmeterol in patients with COPD. Thorax 2003; 58: 399–404
Scanlon PD, Connett JE, Waller LA, et al. Smoking cessation and lung function in mild-to-moderate chronic obstructive pulmonary disease. The Lung Health Study. Am J Respir Crit Care Med 2000; 161: 381–90
Bateman E, Singh D, Smith D, et al. Efficacy and safety of tiotropium Respimat SMI in COPD in two 1-year randomized studies. Int J Chron Obstruct Pulmon Dis 2010; 5: 197–208
Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med 2002 Jun 15; 21 (11): 1539–58
Higgins JP, Thompson SG, Deeks JJ, et al. Measuring inconsistency in meta-analyses. BMJ 2003; 327: 557–60
Gelman A, Carlin JB, Stern HS, et al. Bayesian data analysis. London: Chapman & Hall, 1995
Carlin BP, Louis TA. Bayes and emperical Bayes methods for data analysis. London: Chapman & Hall, 1996
Kreft IGG, Leeuw JD. Introducing multilevel modeling. Thousand Oaks (CA): Sage, 1998
Spiegenhalter DJ, Best NG, Carlin BP, et al. Bayesian measures of model complexity and fit. J R Stat Soc B 2002; 64: 1–34
Fenwick E, Briggs A. Cost-effectiveness acceptability curves in the dock: case not proven? Med Decis Making 2007; 27: 93–5
Arends LR. Multivariate meta-analysis: modelling the heterogeneity. Mixing apples and oranges: dangerous or delicious? [PhD thesis]. Alblasserdam: Haveka BV, 2006
Ades AE, Sculpher M, Sutton A, et al. Bayesian methods for evidence synthesis in cost-effectiveness analysis. Pharmacoeconomics 2006; 24 (1): 1–19
Gelman A. Prior distributions for variance parameters in hierarchical models. Bayesian Anal 2006; 1: 515–33
Browne WJ, Draper D. A comparison of Bayesian and likelihood-based methods for fitting multilevel models. Bayesian Anal 2006; 1: 473–514
Acknowledgements
This research was sponsored by an unrestricted grant from Boehringer Ingelheim GmbH. The Institute for Medical Technology Assessment has a consultancy agreement with Boehringer Ingelheim GmbH. The Institute also received grants to conduct pharmacoeconomic research on respiratory medicines of Boehringer Ingelheim, Pfizer, GSK and Nycomed. The authors do not hold stock or other equities in pharmaceutical companies. Mark Oppe conducted the analyses and drafted the article. Maiwenn Al gave advice on the analyses and was involved in writing the article. Maureen Rutten-van Mölken was involved in the study design, writing the article and project management.
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Oppe, M., Al, M. & van Rutten-Mölken, M. Comparing Methods of Data Synthesis. Pharmacoeconomics 29, 239–250 (2011). https://doi.org/10.2165/11539870-000000000-00000
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DOI: https://doi.org/10.2165/11539870-000000000-00000