Megan Othus1, Aasthaa Bansal2, Lisel Koepl2, Samuel Wagner3, Scott Ramsey2. 1. Fred Hutchinson Cancer Research Center, Seattle, WA, USA. Electronic address: mothus@fhcrc.org. 2. Fred Hutchinson Cancer Research Center, Seattle, WA, USA. 3. Oncology Division, Bristol-Myers Squibb, New York, NY, USA.
Abstract
BACKGROUND: Economic evaluations often measure an intervention effect with mean overall survival (OS). Emerging types of cancer treatments offer the possibility of being "cured" in that patients can become long-term survivors whose risk of death is the same as that of a disease-free person. Describing cured and noncured patients with one shared mean value may provide a biased assessment of a therapy with a cured proportion. OBJECTIVE: The purpose of this article is to explain how to incorporate the heterogeneity from cured patients into health economic evaluation. METHODS: We analyzed clinical trial data from patients with advanced melanoma treated with ipilimumab (Ipi; n = 137) versus glycoprotein 100 (gp100; n = 136) with statistical methodology for mixture cure models. Both cured and noncured patients were subject to background mortality not related to cancer. RESULTS: When ignoring cured proportions, we found that patients treated with Ipi had an estimated mean OS that was 8 months longer than that of patients treated with gp100. Cure model analysis showed that the cured proportion drove this difference, with 21% cured on Ipi versus 6% cured on gp100. The mean OS among the noncured cohort patients was 10 and 9 months with Ipi and gp100, respectively. The mean OS among cured patients was 26 years on both arms. When ignoring cured proportions, we found that the incremental cost-effectiveness ratio (ICER) when comparing Ipi with gp100 was $324,000/quality-adjusted life-year (QALY) (95% confidence interval $254,000-$600,000). With a mixture cure model, the ICER when comparing Ipi with gp100 was $113,000/QALY (95% confidence interval $101,000-$154,000). CONCLUSIONS: This analysis supports using cure modeling in health economic evaluation in advanced melanoma. When a proportion of patients may be long-term survivors, using cure models may reduce bias in OS estimates and provide more accurate estimates of health economic measures, including QALYs and ICERs.
BACKGROUND: Economic evaluations often measure an intervention effect with mean overall survival (OS). Emerging types of cancer treatments offer the possibility of being "cured" in that patients can become long-term survivors whose risk of death is the same as that of a disease-free person. Describing cured and noncured patients with one shared mean value may provide a biased assessment of a therapy with a cured proportion. OBJECTIVE: The purpose of this article is to explain how to incorporate the heterogeneity from cured patients into health economic evaluation. METHODS: We analyzed clinical trial data from patients with advanced melanoma treated with ipilimumab (Ipi; n = 137) versus glycoprotein 100 (gp100; n = 136) with statistical methodology for mixture cure models. Both cured and noncured patients were subject to background mortality not related to cancer. RESULTS: When ignoring cured proportions, we found that patients treated with Ipi had an estimated mean OS that was 8 months longer than that of patients treated with gp100. Cure model analysis showed that the cured proportion drove this difference, with 21% cured on Ipi versus 6% cured on gp100. The mean OS among the noncured cohort patients was 10 and 9 months with Ipi and gp100, respectively. The mean OS among cured patients was 26 years on both arms. When ignoring cured proportions, we found that the incremental cost-effectiveness ratio (ICER) when comparing Ipi with gp100 was $324,000/quality-adjusted life-year (QALY) (95% confidence interval $254,000-$600,000). With a mixture cure model, the ICER when comparing Ipi with gp100 was $113,000/QALY (95% confidence interval $101,000-$154,000). CONCLUSIONS: This analysis supports using cure modeling in health economic evaluation in advanced melanoma. When a proportion of patients may be long-term survivors, using cure models may reduce bias in OS estimates and provide more accurate estimates of health economic measures, including QALYs and ICERs.
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