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Purpose: Quality-adjusted life-year (QALY) is a dominant measurement of health gain in economic evaluations for pricing drugs. However, end-of-life (EoL) patients' preference for QALY gains in life expectancy (LE) and quality of life (QoL) during different disease stages remains unknown and is seldom involved in decision-making. This study aims to measure preferences and willingness-to-pay (WTP) towards different types of QALY gain among EoL cancer patients. Methods: We attributed QALY gain to four types, gain in LE and QoL, respectively, and during both progression-free survival (PFS) and post-progression survival (PPS). A discrete choice experiment including five attributes (the four QALY attributes and one cost attribute) with three levels each was developed and conducted with 85 Chinese advanced non-small cell lung cancer patients in 2022. All levels were set with QALY gain/cost synthesised from research on anti-lung cancer drugs recently listed by Chinese National Healthcare Security Administration. Each respondent answered six choice tasks in a face-to-face interview. The data were analysed using mixed logit models. Results: Patients valued LE-related QALY gain in PFS most, with a relative importance of 81.8% and a WTP of $43,160 [95% CI 26,751 ~ 59,569] per QALY gain. Respondents consistently preferred LE-related to QoL-related QALY gain regardless of disease stage. Patients with higher income or lower education levels tended to pay more for QoL-related QALY gain. Conclusion: Our findings suggest a prioritised resource allocation to EoL-prolonging health technologies. Given the small sample size and large individual heterogeneity, a full-scale study is needed to provide more robust results. [ABSTRACT FROM AUTHOR]

Background: Serplulimab is a potential valuable therapy, while patients, physicians, and decision-makers are uncertain about the cost-effectiveness of this novel drug and its corresponding reasonable price. This study aimed to simulate the price at which serplulimab was cost-effective as first-line therapy for United States (US) and Chinese extensive-stage small-cell lung cancer (ES-SCLC) patients. Methods: In this economic evaluation, a partitioned survival model was constructed from the perspective of US and Chinese payers. Baseline characteristics of patients and critical clinical data were obtained from ASTRUM-005. Costs and utilities were collected from open-access databases and published literature. Cumulative costs (in US dollars), life years, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs) were measured and compared. Price simulation was conducted to inform the pricing strategy at the given willingness-to-pay (WTP) threshold. The robustness of the model was assessed via sensitivity analyses and scenario analyses; subgroup analyses were also included. Results: Base-case analysis indicated that serplulimab ($818.16/100 mg) would be cost-effective in the US at the WTP threshold of $150,000, with improved effectiveness of 0.61 QALYs and an additional cost of $64,918 (ICER $106,757). Serplulimab ($818.16/100 mg, patient assistance program considered) was cost-effective in China, with improved effectiveness of 0.58 QALYs and an increased overall cost of $19,369 (ICER $33,392). The price simulation results indicated that serplulimab was favored in the US when the price was less than $762.11/100 mg and $1261.57/100 mg at the WTP threshold of $100,000 and $150,000, respectively; it was cost-effective at the WTP threshold of $38,184 when the price was less than $373.37/100 mg in China. Sensitivity analyses revealed that the above results were stable. Subgroup analysis results indicated an overall trend for subgroups with better survival advantages to have a higher probability of cost-effectiveness, despite serplulimab not being cost-effective in some subgroups. Conclusions: Serplulimab might be a valuable and cost-effective therapy in both the US and China. The evidence-based pricing strategy provided by this study could benefit decision-makers in making optimal decisions and clinicians in general clinical practice. More evidence about the budget impact and affordability for patients is needed. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to compare the performance of different extrapolation modeling techniques and analyze their impact on structural uncertainties in the economic evaluations of cancer immunotherapy.The individual patient data was reconstructed through published Checkmate 067 Kaplan Meier curves. Standard parametric models and six flexible techniques were tested, including fractional polynomial, restricted cubic splines, Royston–Parmar models, generalized additive models, parametric mixture models, and mixture cure models. Mean square errors (MSE) and bias from raw survival plots were used to test the model fitness and extrapolation performance. Variability of estimated incremental cost-effectiveness ratios (ICERs) from different models was used to inform the structural uncertainty in economic evaluations. All indicators were analyzed and compared under cut-offs of 3 years and 6.5 years, respectively, to further discuss model impact under different data maturity. R Codes for reproducing this study can be found on GitHub.The flexible techniques in general performed better than standard parametric models with smaller MSE irrespective of the data maturity. Survival outcomes projected by long-term extrapolation using immature data differed from those with mature data. Although a best-performing model was not found because several models had very similar MSE in this case, the variability of modeled ICERs significantly increased when prolonging simulation cycles.Flexible techniques show better performance in the case of Checkmate 067, regardless of data maturity. Model choices affect ICERs of cancer immunotherapy, especially when dealing with immature survival data. When researchers lack evidence to identify the ‘right’ model, we recommend identifying and revealing the model impacts on structural uncertainty.Methods: The aim of this study was to compare the performance of different extrapolation modeling techniques and analyze their impact on structural uncertainties in the economic evaluations of cancer immunotherapy.The individual patient data was reconstructed through published Checkmate 067 Kaplan Meier curves. Standard parametric models and six flexible techniques were tested, including fractional polynomial, restricted cubic splines, Royston–Parmar models, generalized additive models, parametric mixture models, and mixture cure models. Mean square errors (MSE) and bias from raw survival plots were used to test the model fitness and extrapolation performance. Variability of estimated incremental cost-effectiveness ratios (ICERs) from different models was used to inform the structural uncertainty in economic evaluations. All indicators were analyzed and compared under cut-offs of 3 years and 6.5 years, respectively, to further discuss model impact under different data maturity. R Codes for reproducing this study can be found on GitHub.The flexible techniques in general performed better than standard parametric models with smaller MSE irrespective of the data maturity. Survival outcomes projected by long-term extrapolation using immature data differed from those with mature data. Although a best-performing model was not found because several models had very similar MSE in this case, the variability of modeled ICERs significantly increased when prolonging simulation cycles.Flexible techniques show better performance in the case of Checkmate 067, regardless of data maturity. Model choices affect ICERs of cancer immunotherapy, especially when dealing with immature survival data. When researchers lack evidence to identify the ‘right’ model, we recommend identifying and revealing the model impacts on structural uncertainty.Results: The aim of this study was to compare the performance of different extrapolation modeling techniques and analyze their impact on structural uncertainties in the economic evaluations of cancer immunotherapy.The individual patient data was reconstructed through published Checkmate 067 Kaplan Meier curves. Standard parametric models and six flexible techniques were tested, including fractional polynomial, restricted cubic splines, Royston–Parmar models, generalized additive models, parametric mixture models, and mixture cure models. Mean square errors (MSE) and bias from raw survival plots were used to test the model fitness and extrapolation performance. Variability of estimated incremental cost-effectiveness ratios (ICERs) from different models was used to inform the structural uncertainty in economic evaluations. All indicators were analyzed and compared under cut-offs of 3 years and 6.5 years, respectively, to further discuss model impact under different data maturity. R Codes for reproducing this study can be found on GitHub.The flexible techniques in general performed better than standard parametric models with smaller MSE irrespective of the data maturity. Survival outcomes projected by long-term extrapolation using immature data differed from those with mature data. Although a best-performing model was not found because several models had very similar MSE in this case, the variability of modeled ICERs significantly increased when prolonging simulation cycles.Flexible techniques show better performance in the case of Checkmate 067, regardless of data maturity. Model choices affect ICERs of cancer immunotherapy, especially when dealing with immature survival data. When researchers lack evidence to identify the ‘right’ model, we recommend identifying and revealing the model impacts on structural uncertainty.Conclusions: The aim of this study was to compare the performance of different extrapolation modeling techniques and analyze their impact on structural uncertainties in the economic evaluations of cancer immunotherapy.The individual patient data was reconstructed through published Checkmate 067 Kaplan Meier curves. Standard parametric models and six flexible techniques were tested, including fractional polynomial, restricted cubic splines, Royston–Parmar models, generalized additive models, parametric mixture models, and mixture cure models. Mean square errors (MSE) and bias from raw survival plots were used to test the model fitness and extrapolation performance. Variability of estimated incremental cost-effectiveness ratios (ICERs) from different models was used to inform the structural uncertainty in economic evaluations. All indicators were analyzed and compared under cut-offs of 3 years and 6.5 years, respectively, to further discuss model impact under different data maturity. R Codes for reproducing this study can be found on GitHub.The flexible techniques in general performed better than standard parametric models with smaller MSE irrespective of the data maturity. Survival outcomes projected by long-term extrapolation using immature data differed from those with mature data. Although a best-performing model was not found because several models had very similar MSE in this case, the variability of modeled ICERs significantly increased when prolonging simulation cycles.Flexible techniques show better performance in the case of Checkmate 067, regardless of data maturity. Model choices affect ICERs of cancer immunotherapy, especially when dealing with immature survival data. When researchers lack evidence to identify the ‘right’ model, we recommend identifying and revealing the model impacts on structural uncertainty. [ABSTRACT FROM AUTHOR]