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
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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).
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.
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.
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.
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.
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.
The authors thank the two anonymous reviewers for their constructive comments and suggestions.
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.
Compliance with Ethical Standards
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.
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