Abstract
Aims:
The aim of this study was to estimate the cost-effectiveness of nivolumab versus everolimus for second-line treatment of renal cell carcinoma (RCC) based on mature data, updated safety and decreased everolimus price.
Materials and methods:
A 3-state (pre-progression/progression-free disease, progressive disease and death) Markov model was developed from the perspective of the Australian health care system. Two scenarios were tested. Scenario 1 used 30-months clinical data and scenario 2 used updated 80-months clinical data with updated everolimus price. Inputs for quality-of-life and costs were informed by the literature and government sources. Incremental cost-effectiveness ratio (ICER) per quality adjusted life years (QALY) gained was reported and an ICER threshold of AU$75,000 was assumed. Threshold analysis was performed, and uncertainty was explored using one-way and probabilistic sensitivity analyses.
Results:
In scenario 1, the model estimated 1.73 QALYs at a cost of AU$105,000 for nivolumab and 1.48 QALYs at AU$38,000 for everolimus with an ICER = AU$266,871/QALY gained. A rebate of 54.4% was needed for nivolumab to reach the ICER threshold. For scenario 2, 1.93 QALYs at AU$111,418 was estimated for nivolumab and 1.60 QALYs at AU$31,942 for everolimus with an ICER of AU$213,320/QALY gained. The rebate needed to reach the ICER threshold was 54.9%. One-way sensitivity analyses for both scenarios showed that the cost of nivolumab, time horizon and utilities were main drivers. The cost-effectiveness acceptability curves highlighted the differences in cost-effectiveness of the two scenarios, as well as significant uncertainty in the results.
Conclusions:
A 54% rebate of the published price is needed for nivolumab to be cost-effective in Australia for the treatment of RCC. At that rebate, nivolumab remains cost-effective despite severe price erosion of everolimus because of improved longer term follow-up data. We recommend that generic price erosion should be accounted for when performing cost-effectiveness analysis.
The aim of this study was to estimate the cost-effectiveness of nivolumab versus everolimus for second-line treatment of renal cell carcinoma (RCC) based on mature data, updated safety and decreased everolimus price.
Materials and methods:
A 3-state (pre-progression/progression-free disease, progressive disease and death) Markov model was developed from the perspective of the Australian health care system. Two scenarios were tested. Scenario 1 used 30-months clinical data and scenario 2 used updated 80-months clinical data with updated everolimus price. Inputs for quality-of-life and costs were informed by the literature and government sources. Incremental cost-effectiveness ratio (ICER) per quality adjusted life years (QALY) gained was reported and an ICER threshold of AU$75,000 was assumed. Threshold analysis was performed, and uncertainty was explored using one-way and probabilistic sensitivity analyses.
Results:
In scenario 1, the model estimated 1.73 QALYs at a cost of AU$105,000 for nivolumab and 1.48 QALYs at AU$38,000 for everolimus with an ICER = AU$266,871/QALY gained. A rebate of 54.4% was needed for nivolumab to reach the ICER threshold. For scenario 2, 1.93 QALYs at AU$111,418 was estimated for nivolumab and 1.60 QALYs at AU$31,942 for everolimus with an ICER of AU$213,320/QALY gained. The rebate needed to reach the ICER threshold was 54.9%. One-way sensitivity analyses for both scenarios showed that the cost of nivolumab, time horizon and utilities were main drivers. The cost-effectiveness acceptability curves highlighted the differences in cost-effectiveness of the two scenarios, as well as significant uncertainty in the results.
Conclusions:
A 54% rebate of the published price is needed for nivolumab to be cost-effective in Australia for the treatment of RCC. At that rebate, nivolumab remains cost-effective despite severe price erosion of everolimus because of improved longer term follow-up data. We recommend that generic price erosion should be accounted for when performing cost-effectiveness analysis.
| Original language | English |
|---|---|
| Pages (from-to) | 893-899 |
| Number of pages | 7 |
| Journal | Journal of Medical Economics |
| Volume | 24 |
| Issue number | 1 |
| DOIs | |
| Publication status | E-pub ahead of print - 29 Jul 2021 |
| Externally published | Yes |
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