Abstract
Background
In Ireland, all new drugs for which reimbursement by the healthcare payer is sought undergo a health technology assessment by the National Centre for Pharmacoeconomics. The National Centre for Pharmacoeconomics estimate expected value of perfect information but not partial expected value of perfect information (owing to computational expense associated with typical methodologies).
Objective
The objective of this study was to examine the feasibility and utility of estimating partial expected value of perfect information via a computationally efficient, non-parametric regression approach.
Methods
This was a retrospective analysis of evaluations on drugs for cancer that had been submitted to the National Centre for Pharmacoeconomics (January 2010 to December 2014 inclusive). Drugs were excluded if cost effective at the submitted price. Drugs were excluded if concerns existed regarding the validity of the applicants’ submission or if cost-effectiveness model functionality did not allow required modifications to be made. For each included drug (n = 14), value of information was estimated at the final reimbursement price, at a threshold equivalent to the incremental cost-effectiveness ratio at that price. The expected value of perfect information was estimated from probabilistic analysis. Partial expected value of perfect information was estimated via a non-parametric approach. Input parameters with a population value at least €1 million were identified as potential targets for research.
Results
All partial estimates were determined within minutes. Thirty parameters (across nine models) each had a value of at least €1 million. These were categorised. Collectively, survival analysis parameters were valued at €19.32 million, health state utility parameters at €15.81 million and parameters associated with the cost of treating adverse effects at €6.64 million. Those associated with drug acquisition costs and with the cost of care were valued at €6.51 million and €5.71 million, respectively.
Conclusion
This research demonstrates that the estimation of partial expected value of perfect information via this computationally inexpensive approach could be considered feasible as part of the health technology assessment process for reimbursement purposes within the Irish healthcare system. It might be a useful tool in prioritising future research to decrease decision uncertainty.
Similar content being viewed by others
References
Hoomans T, Fenwick E, Palmer S, Claxton K. Value of information and value of implementation: application of an analytic framework to inform resource allocation decisions in metastatic hormone-refractory prostate cancer. Value Health. 2009;12(2):315–24.
Claxton K, Sculpher M. Using value of information analysis to prioritise health research: some lessons from recent UK experience. Pharmacoeconomics. 2006;24(11):1055–68.
Claxton K, Neumann P, Araki S, Weinstein M. Bayesian value of information analysis: an application to a policy model of Alzheimer’s disease. Int J Technol Assess Health Care. 2001;17(1):38–55.
Fenwick E, Palmer S, Claxton K, Sculpher M, Abrams K, Sutton A. An iterative Bayesian approach to health technology assessment: application to a policy of preoperative optimization for patients undergoing major elective surgery. Med Decis Mak. 2006;26:480–96.
Brennan A, Kharroubi S, O’Hagan A, Chilcott J. Calculating partial expected value of perfect information via Monte Carlo sampling algorithms. Med Decis Mak. 2007;27(4):448–70.
Claxton K. Exploring uncertainty in cost-effectiveness analysis. Pharmacoeconomics. 2008;26(9):781–98.
Groot-Koerkamp B, Hunink M, Stijnen T, Weinstein M. Identifying key parameters in cost-effectiveness analysis using value of information: a comparison of methods. Health Econ. 2006;15:383–92.
Brennan A, Kharroubi S, O’Hagan A, Chilcott J. Calculating partial expected value of perfect information via Monte Carlo sampling algorithms. Med Decis Mak. 2007;27:448–70.
Heath A, Manolopoulou I, Baio G. A review of methods for analysis of the expected value of information. Med Decis Mak. 2017. doi:10.1177/0272989X17697692.
Strong M, Oakley J, Brennan A. Estimating multi-parameter partial expected value of perfect information from a probabilistic sensitivity analysis sample: a non-parametric regression approach. Med Decis Mak. 2014;34(3):311–26.
Heath A, Manolopoulou I, Baio G. Estimating the expected value of partial perfect information in health economic evaluations using integrated nested Laplace approximation. Stat Med. 2016;35(23):4264–80.
Strong M. Section of Public Health, School of Health and Related Research, University of Sheffield. R code for partial EVPI functions. Available from: http://www.shef.ac.uk/scharr/sections/ph/staff/profiles/mark. Accessed 16 Nov 2016.
Strong M, Breeze P, Thomas C, Brennan A. SAVI: Sheffield accelerated value of information. Release version 2.0.16 (2016-09-29). University of Sheffield. Available from: http://savi.shef.ac.uk/SAVI/. Accessed 21 Jun 2017.
Baio G, Hadjipanayiotou P, Berardi A, Heath A. BCEAweb: a web interface to the R package BCEA. Available from: https://egon.stats.ucl.ac.uk/projects/BCEAweb/. Accessed 20 Jun 2017.
National Centre for Pharmacoeconomics, Dublin, Ireland. Available from: http://www.ncpe.ie. Accessed 8 Mar 2017.
Health Information and Quality Authority. Guidelines for evaluating the clinical effectiveness of health technologies in Ireland. 24 November, 2011. Available from: http://www.hiqa.ie/publications/guidelines-evaluating-clinical-effectiveness-health-technologies-ireland. Accessed 22 Feb 2015.
Health Information and Quality Authority. Guidelines for the economic evaluation of health technologies in Ireland. 11 February, 2014. Available from: http://www.hiqa.ie/publications/guidelines-economic-evaluation-healthtechnologies-ireland. Accessed 6 Jun 2017.
Health Information and Quality Authority. Guidelines for the budget impact analysis of health technologies in Ireland. 11 February, 2014. Available from: http://www.hiqa.ie/publications/guidelines-budget-impact-analysis-health-technologies-ireland-0. Accessed 29 Feb 2016.
McCullagh L, Walsh C, Barry M. Value-of-information analysis to reduce decision uncertainty associated with the choice of thromboprophylaxis after total hip replacement in the Irish healthcare setting. Pharmacoeconomics. 2012;30(10):941–59.
McCullagh L, Barry M. The pharmacoeconomic evaluation process in Ireland. Pharmacoeconomics. 2016;34(12):1267–76.
Irish Pharmaceutical Healthcare Association. Framework agreement on the supply of medicines to the health services 2016–2020: agreement on the supply and pricing of medicines to the health services. Available from: http://www.ipha.ie/alist/ipha-hse-agreement.aspx. Accessed 9 Mar 2017.
Schmitz S, McCullagh L, Adams R, Barry M, Walsh C. Identifying and revealing the importance of decision-making criteria for health technology assessment: a retrospective analysis of reimbursement recommendations in Ireland. Pharmacoeconomics. 2016;34(9):925–37.
European Commission. Directorate General for Health and Food Safety, Brussels, Belgium. Register of designated orphan medicinal products. Pharmaceuticals: community register. Available from: http://ec.europa.eu/health/documents/community-register/html/orphreg.htm. Accessed 19 May 2016.
Claxton K. The irrelevance of interference: a decision-making approach to the stochastic evaluation of health care technologies. J Health Econ. 1999;18:341–64.
Godman B, Malmstram RE, Diogene E, Gray A, Jayathissa S, Timoney A, et al. Are new models needed to optimize the utilization of new medicines to sustain healthcare systems? Expert Rev Clin Pharmacol. 2015;8(1):77–94.
Dolan E, Schmitz S, Barry M, McCullagh L. Movement of drugs for cancer through the pharmacoeconomic evaluation process in Ireland: consideration given to the quality of clinical evidence. Poster abstract under review for ISPOR 19th Annual European Congress, 29 October to 2 November 2016, Vienna.
Reddy B, Walsh C, Barry M, Adams R. Results of the Eq-5d-3l valuation project for Ireland. Value Health. 2015;18(7):A740.
Reddy B, Walsh C, Barry M, Adams R. Experienced vs. hypothetical health states preferences using Eq-5d 3L and 5L versions: a national study. Value Health. 2015;18(7):A741.
Malmström R, Godman B, Diogene E, Baumgärtel C, Bennie M, Bishop I, et al. Dabigatran a case history demonstrating the need for comprehensive approaches to optimize the use of new drugs. Front Pharmacol. 2013;4:39.
Office of the Attorney General, Government of Ireland, Oireachtas. Health (Pricing and Supply of Medical Goods) Act 2013, Number 14. Available from: http://www.irishstatutebook.ie/eli/2013/act/14/enacted/en/html. Accessed 1 Jun 2017.
Yokota F, Thompson K. Value of information literature analysis: a review of applications in health risk management. Med Decis Mak. 2004;24:287–98.
Philips Z, Claxton K, Palmer S. The half-life of truth: what are appropriate time horizons for research decisions? Med Decis Mak. 2008;28:287–99.
Poynard T, Munteanu M, Ratziu V, Benhamou Y, Di-Martino V, Taieb J. Truth survival in clinical research: an evidence-based requiem? Ann Intern Med. 2002;136:888–95.
LaValley MP, Felson D. Truth survival. Ann Intern Med. 2002;137(11):932 (author reply 932).
Data Availability Statement
The data on which this analysis is based come from probabilistic analyses of models that cannot be made available as supplementary material owing to confidentiality concerns. The code on which the analyses are based is available at http://www.shef.ac.uk/scharr/sections/ph/staff/profiles/mark. For readers who wish to implement this approach using outputs from their own models (where outputs may be available within Excel or as .csv files), please contact the corresponding author for guidance.
Acknowledgements
The authors thank the two anonymous reviewers for their constructive comments and suggestions.
Author information
Authors and Affiliations
Contributions
The concept for this article was conceived by LMcC. LMcC gathered the literature, performed all analyses and drafted the manuscript. SS and CW provided advice on methodologies and the presentation of results. MB provided insight on the utility of this analysis to the decision maker. SS, MB and CW reviewed and commented on various drafts of the manuscript.
Corresponding author
Ethics declarations
Funding
CW was supported by the Health Research Board RL2013/04 Research Leader Award.
Conflict of interest
LMcC, SS, MB and CW have no conflicts of interest directly relevant to the content of this article.
Rights and permissions
About this article
Cite this article
McCullagh, L., Schmitz, S., Barry, M. et al. Examining the Feasibility and Utility of Estimating Partial Expected Value of Perfect Information (via a Nonparametric Approach) as Part of the Reimbursement Decision-Making Process in Ireland: Application to Drugs for Cancer. PharmacoEconomics 35, 1177–1185 (2017). https://doi.org/10.1007/s40273-017-0552-y
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40273-017-0552-y