Hostname: page-component-8448b6f56d-c4f8m Total loading time: 0 Render date: 2024-04-23T13:03:28.276Z Has data issue: false hasContentIssue false

SYNTHESIS OF EVIDENCE FOR REIMBURSEMENT DECISIONS: A BAYESIAN REANALYSIS

Published online by Cambridge University Press:  26 November 2014

Willem Woertman
Affiliation:
Department for Health Evidence, Radboud University Nijmegen Medical Centrewillem.woertman@radboudumc.nl
Rene Sluiter
Affiliation:
Department for Health Evidence, Radboud University Nijmegen Medical Centrewillem.woertman@radboudumc.nl
Gert Jan van der Wilt
Affiliation:
Department for Health Evidence, Radboud University Nijmegen Medical Centrewillem.woertman@radboudumc.nl

Abstract

Objectives: The aim of this study was to compare Bayesian methods with the standard methods that are used for evidence-based policy making.

Methods: We performed a Bayesian reanalysis of the data underlying a reimbursement advice by the Dutch National Health Insurance Board (CVZ) regarding the anti-diabetic drug exenatide (an alternative to insulin). We synthesized evidence from various sources that was available when the CVZ advice was drafted: expert opinion (as elicited from internists), experimental data (from direct comparison studies), and observational data. Subsequently, the original frequentist results and the results from the Bayesian reanalysis were compared in terms of outcomes and interpretations. These results were presented in a meeting with staff from CVZ, whose opinions about the usefulness of a Bayesian approach were assessed using a questionnaire.

Results: The Bayesian approach yields outcomes that summarize different pieces of evidence, which would have been difficult to obtain otherwise. Moreover, there are conceptual differences, and the Bayesian approach allows for determining probabilities of clinically relevant differences. The staff at CVZ were fairly positive with respect to the use of Bayesian methods, although practical barriers were also seen as important.

Conclusions: The Bayesian outcomes are different and could be more suited to the informational needs of policy makers. The response from staff at CVZ provides some support for this statement, but more research at the interface of science and policy is needed.

Type
Methods
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Spiegelhalter, DJ, Abrams, KR, Myles, JP. Bayesian approaches to clinical trials and health-care evaluation. Chichester: John Wiley; 2004.Google Scholar
2. Harrell, FE Jr, Shih, YC. Using full probability models to compute probabilities of actual interest to decision makers. Int J Technol Assess Health Care. 2001;17:1726.Google Scholar
3. Lilford, RJ, Braunholtz, D. Who's afraid of Thomas Bayes? J Epidemiol Community Health. 2000;54:731739.Google Scholar
4. Spiegelhalter, DJ, Abrams, KR, Myles, JP. Bayesian methods in health technology assessment: A review. Health Technol Assess. 2000;4:1130.CrossRefGoogle ScholarPubMed
5. van der Wilt, GJ, Rovers, M, Straatman, H, van der Bij, S, van den Broek, P, Zielhuis, GA. Policy relevance of Bayesian statistics overestimated? Int J Technol Assess Health Care. 2004;20:488492.Google Scholar
6. Sutton, AJ, Abrams, KR. Bayesian methods in meta-analysis and evidence synthesis. Stat Methods Med Res. 2001;10:277303.Google Scholar
7. Allmark, P. Bayes and health care research. Med Health Care Philos. 2004;7:321332.Google Scholar
8. Woertman, WH, Vermeulen, B, Groenewoud, H, et al. Evidence based policy decisions through a Bayesian approach: The case of a statin appraisal in the Netherlands. Health Policy. 2013;112:234240.Google Scholar
9. CVZ. Farmaceutisch kompas. 2011. http://www.fk.cvz.nl/ (accessed April 28, 2011).Google Scholar
10. College voor Zorgverzekeringen. Beoordeling exenatide (Byetta), 2007. http://www.cvz.nl/binaries/live/cvzinternet/hst_content/nl/documenten/cfh-rapporten/2007/cfh0709+exenatide+byetta.pdf (accessed April 28, 2011).Google Scholar
11. College voor Zorgverzekeringen. Herbeoordeling exenatide (Byetta), 2008. http://www.cvz.nl/binaries/live/cvzinternet/hst_content/nl/documenten/cfh-rapporten/2009/cfh0901+exenatide+byetta.pdf (accessed April 28, 2011).Google Scholar
12. Eddy, DM. The confidence profile method: A Bayesian method for assessing health technologies. Oper Res. 1989;37:210228.CrossRefGoogle Scholar
13. Eddy, DM, Hasselblad, V, Shachter, R. Meta-analysis by the confidence profile method: The statistical synthesis of evidence. San Diego: Academic Press; 1992.Google Scholar
14. Barnett, AH, Burger, J, Johns, D, et al. Tolerability and efficacy of exenatide and titrated insulin glargine in adult patients with type 2 diabetes previously uncontrolled with metformin or a sulfonylurea: A multinational, randomized, open-label, two-period, crossover noninferiority trial. Clin Ther. 2007;29:23332348.Google Scholar
15. Heine, RJ, Van Gaal, LF, Johns, D, et al. Exenatide versus insulin glargine in patients with suboptimally controlled type 2 diabetes: A randomized trial. Ann Intern Med. 2005 18;143:559569.Google Scholar
16. Nauck, MA, Duran, S, Kim, D, et al. A comparison of twice-daily exenatide and biphasic insulin aspart in patients with type 2 diabetes who were suboptimally controlled with sulfonylurea and metformin: A non-inferiority study. Diabetologia. 2007;50:259267.Google Scholar
17. Buse, JB, Henry, RR, Han, J, et al. Effects of exenatide (exendin-4) on glycemic control over 30 weeks in sulfonylurea-treated patients with type 2 diabetes. Diabetes Care. 2004;27:26282635.CrossRefGoogle ScholarPubMed
18. Defronzo, RA, Ratner, RE, Han, J, et al. Effects of exenatide (exendin-4) on glycemic control and weight over 30 weeks in metformin-treated patients with type 2 diabetes. Diabetes Care. 2005;28:10921100.Google Scholar
19. Kendall, DM, Riddle, MC, Rosenstock, J, et al. Effects of exenatide (exendin-4) on glycemic control over 30 weeks in patients with type 2 diabetes treated with metformin and a sulfonylurea. Diabetes Care. 2005;28:10831091.Google Scholar
20. Holman, RR, Thorne, KI, Farmer, AJ, et al. Addition of biphasic, prandial, or basal insulin to oral therapy in type 2 diabetes. N Engl J Med. 2007 25;357:17161730.CrossRefGoogle Scholar
21. Makimattila, S, Nikkila, K, Yki-Jarvinen, H. Causes of weight gain during insulin therapy with and without metformin in patients with Type II diabetes mellitus. Diabetologia. 1999;42:406412.Google Scholar
22. Lu, G, Ades, AE. Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med. 2004;23:31053124.CrossRefGoogle ScholarPubMed
Supplementary material: File

Woertman Supplementary Material

Figure S1

Download Woertman Supplementary Material(File)
File 26.1 KB