Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-02T20:30:15.772Z Has data issue: false hasContentIssue false
  • English
  • Français

USING PREDICTION INTERVALS FROM RANDOM-EFFECTS META-ANALYSES IN AN ECONOMIC MODEL

Part of: Special Collection - Methods

Published online by Cambridge University Press:  29 January 2014

Conor Teljeur ,
Michelle O'Neill ,
Patrick Moran ,
Linda Murphy ,
Patricia Harrington ,
Máirín Ryan  and
Martin Flattery
Conor Teljeur
Affiliation:
Health Information and Quality Authority
Michelle O'Neill
Affiliation:
Health Information and Quality Authority
Patrick Moran
Affiliation:
Health Information and Quality Authority
Linda Murphy
Affiliation:
Health Information and Quality Authority
Patricia Harrington
Affiliation:
Health Information and Quality Authority
Máirín Ryan
Affiliation:
Health Information and Quality Authority
Martin Flattery
Affiliation:
Baxter Healthcare Corporation
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: When incorporating treatment effect estimates derived from a random-effect meta-analysis it is tempting to use the confidence bounds to determine the potential range of treatment effect. However, prediction intervals reflect the potential effect of a technology rather than the more narrowly defined average treatment effect. Using a case study of robot-assisted radical prostatectomy, this study investigates the impact on a cost-utility analysis of using clinical effectiveness derived from random-effects meta-analyses presented as confidence bounds and prediction intervals, respectively.

Methods: To determine the cost-utility of robot-assisted prostatectomy, an economic model was developed. The clinical effectiveness of robot-assisted surgery compared with open and conventional laparoscopic surgery was estimated using meta-analysis of peer-reviewed publications. Assuming treatment effect would vary across studies due to both sampling variability and differences between surgical teams, random-effects meta-analysis was used to pool effect estimates.

Results: Using the confidence bounds approach the mean and median ICER was €24,193 and €26,731/QALY (95%CI: €13,752 to €68,861/QALY), respectively. The prediction interval approach produced an equivalent mean and median ICER of €26,920 and €26,643/QALY (95%CI: -€135,244 to €239,166/QALY), respectively. Using prediction intervals, there is a probability of 0.042 that robot-assisted surgery will result in a net reduction in QALYs.

Conclusions: Using prediction intervals rather than confidence bounds does not affect the point estimate of the treatment effect. In meta-analyses with significant heterogeneity, the use of prediction intervals will produce wider ranges of treatment effect, and hence result in greater uncertainty, but a better reflection of the effect of the technology.

Keywords

Meta-analysisStatisticsEconomic modelsTreatment effectivenessProstatectomy
Type
Methods
Information
International Journal of Technology Assessment in Health Care , Volume 30 , Issue 1 , January 2014 , pp. 44 - 49
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. Higgins, JP, Thompson, SG, Spiegelhalter, DJ. A re-evaluation of random-effects meta-analysis. J R Stat Soc Ser A Stat Soc. 2009;172:137–159.Google Scholar
2. Higgins, JP. Commentary: Heterogeneity in meta-analysis should be expected and appropriately quantified. Int J Epidemiol. 2008;37:1158–1160.CrossRefGoogle ScholarPubMed
3. Health Information and Quality Authority. Health technology assessment of robot-assisted surgery in selected surgical procedures. Dublin: Health Information and Quality Authority; 2011.Google Scholar
4. Health Information and Quality Authority. Guidelines for the economic evaluation of health technologies in Ireland. Dublin: Health Information and Quality Authority; 2010.Google Scholar
5. Ho, C, Tsakonas, E, Tran, K, et al. Robot-assisted surgery compared with open surgery and laparoscopic surgery: Clinical effectiveness and economic analyses. Ottawa: Canadian Agency for Drugs and Technologies in Health; 2011.Google ScholarPubMed
6. Riley, RD, Higgins, JP, Deeks, JJ. Interpretation of random effects meta-analyses. BMJ. 2011;342:d549.Google Scholar
7. Wang, H, Zhao, H. A study on confidence intervals for incremental cost-effectiveness ratios. Biom J. 2008;50:505–514.Google Scholar
8. Foundation, R for Statistical Computing. R: A language and environment for statistical computing [computer program]. Vienna, Austria: R Foundation for Statistical Computing; 2011.Google Scholar
9. Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J Stat Softw. 2010;36:1–48.Google Scholar
10. Hohwü, L, Borre, M, Ehlers, L, Venborg Pedersen, K. A short-term cost-effectiveness study comparing robot-assisted laparoscopic and open retropubic radical prostatectomy. J Med Econ. 2011;23:403–409.CrossRefGoogle Scholar
11. O'Malley, SP, Jordan, E. Review of a decision by the Medical Services Advisory Committee based on health technology assessment of an emerging technology: The case for remotely assisted radical prostatectomy. Int J Technol Assess Health Care. 2007;23:286–291.CrossRefGoogle ScholarPubMed