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Pharmacoeconomics Research UnitRESEARCH. DECISION SUPPORT. KNOWLEDGE TRANSLATION. CAPACITY BUILDING .
Exploring the Economic Components of Oncology Drug Reviews for Public Reimbursement
Lisa Masucci1, Jaclyn Beca2, Mona Sabharwal3, Jeffrey Hoch4
St. Michael’s Hospital1, Cancer Care Ontario2, Rexall3, University of California4
CADTH SymposiumApril 11th, 2016
Disclosure• I have no actual or potential conflict of interest in relation to this
topic or presentation.
Background • The pan-Canadian Oncology Drug Review (pCODR) was
established in 2011 to assess cancer drugs and make recommendations to provinces and territories (except Quebec) to guide their funding decisions.
• pCODR uses a deliberative framework and takes into consideration the drug’s overall clinical benefit, cost-effectiveness, alignment with patient values, and feasibility of adoption into health systems.
• The submitter provides an economic evaluation of the drug and economic reviewers review the submitted model.
Objectives• To compare the incremental cost effectiveness ratios (ICERs)
submitted by manufacturers and the economic guidance panel (EGP) at pCODR.
• To assess relationships between funding recommendation and economic elements such as the number and type of issues identified in the economic review or the magnitude of the ICER.
• To assess relationships between the magnitude of the ICER provided by the EGP as well as ICER difference and time to a funding decision by the provinces.
Methods I• Publicly available Economic Guidance Reports were searched:
• Published between July 2011 (inception) and June 2014 • Had a final funding recommendation (34 reviews, 39 indications)• Independently examined by two study authors
• Both study authors abstracted the major issues found within the reports and together grouped them into types. Each issue was also categorized based on the economic reviewer’s actions.
• The final funding recommendations, the submitter and EGP ICER estimates, and the time to a decision for each province were collected.
Methods II• In order to determine associations with a positive funding
recommendation, a logistic regression was conducted in STATA.
• We compared two types of funding recommendations:• Recommend versus Do Not Recommend• Conditionally Recommend versus Recommend
• We examined the following variables: • The EGP’s reported best estimate of the ICER• Whether issues were addressed by the reviewer• Specific types of issues: time horizon, costing, utility
estimates, and model structure
Methods III• We examined whether the ICER provided by the EGP is associated
with time to a funding decision using a linear regression in STATA. • We also examined whether a difference in ICER is associated with a
time to funding decision.• We controlled for the data points for each province.
• The time to a funding decision was calculated as the time from when pCODR posted the notification to implement to the time a decision was reached by the province. Data were obtained from pCODR, as reported by each province.
Results- Funding Recommendation Type (n=39)*
*Drugs captured between July 2011-June 2014
Results
020
0000
4000
0060
0000
8000
00S
ubm
itter
's IC
ER
0 200000 400000 600000 800000Economic Reviewer's ICER
*Note: when a range was reported for the ICER, the higher value was plotted.
Miners et al. 2005. Comparing estimates of cost effectiveness submitted to the National Institute for Clinical Excellence (NICE) by different organisations: retrospective study. BMJ Jan 8; 330(7482): 65.
Miners et al. 2005
Results-Recommend vs. Do not Recommend• In the logistic regression, we observed a relationship with the magnitude of
the EGP’s ICER when comparing Recommend vs. Do Not Recommend (n=16).
• There was an inverse relationship observed between a positive recommendation and the magnitude of the ICER in the review. As the ICER increased, the probability of a positive funding recommendation decreased:
ICER value Predicted probability
P>|z| 95% Confidence Interval
$50,000 0.89 0.00 0.68-1.00*
$100,000 0.72 0.00 0.43-1.00*
$150,000 0.45 0.01 0.09-0.81*Truncated to 1.00 because outside of the plausible range.
Results-Recommend vs. Do not Recommend• We found that reviews that did not have an issue with time horizon
were more likely to have a positive funding recommendation (probability=0.87, 95% CI 0.64-1.00) as well as reviews that did not have an issue with model structure (probability=0.75, 95% CI 0.50-0.99).
• We did not find the frequency of issues that the economic reviewer could not address to be associated with the funding recommendation. Issues with costing and utilities were also not associated with the funding recommendation.
Results-Recommend vs. Conditional• In the logistic regression, we also observed a relationship with the magnitude
of the EGP’s ICER when comparing Recommend vs. Conditional (n=28).
• There was an inverse relationship observed between a positive recommendation and the magnitude of the ICER in the review. As the ICER increased, the probability of a positive funding recommendation decreased:
ICER value Predicted probability
P>|z| 95% Confidence Interval
$50,000 0.97 0.00 0.90-1.00*
$100,000 0.78 0.00 0.46-1.00*
$150,000 0.22 0.13 0.06-0.50
*Truncated to 1.00 because outside of the plausible range.
Results- Recommend vs. Conditional• We did not find a significant association between the frequency of
issues that the economic reviewer could not address and the funding recommendation. As well, we did not find a significant association between any of the specific issues and funding recommendation.
Mean number of days to funding decision
148135
111193
123311
294
224
182
Results- ICER and Time to funding
Coefficient P> I t I 95% Confidence Interval
1.73 0.016 0.42-3.02
The ICER of the EGP has a significant impact on the time to funding. For every $10,000 increase in the ICER the time to funding increased by 1.73 days.
Coefficient P> I t I 95% Confidence Interval4.11 0.003 1.80-6.41
ICER Difference and time to funding
EGP ICER and time to funding
The ICER difference has a significant impact on the time to funding. For every $10,000 increase in the ICER the time to funding increased by 4.11 days.
Results- ICER and Time to funding
Coefficient P> I t I 95% Confidence Interval
0.40 0.600 -2.07-1.28
Submitter’s ICER and time to funding
The submitter’s ICER did not have significant impact on the time to funding.
Limitations• Small sample size (n=39)
• These findings are observed relationships but are not causative. In the publicly posted final recommendations, the reasons a review results in Do Not Recommend are typically described as related to quality and magnitude of clinical benefit.
• There are other major factors that are considered in forming each recommendation - clinical benefit, alignment with patient values, and adoption feasibility into the health care system.
Conclusion• Importance of rigorous economic evaluation by submitter and EGP
• Though not necessarily causative, there appears to be some relationship or association with outcomes (recommendation, time to a funding decision).
• The observed association between the ICER and funding recommendations may be related to many factors. In particular, the magnitude of clinical benefit may contribute to this pattern.
