Shao Taihang, Zhao Mingye, Liang Leyi, Shi Lizheng, Tang Wenxi
School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing, 211198, China.
Center for Pharmacoeconomics and Outcomes Research, China Pharmaceutical University, Nanjing, 211198, China.
Pharmacoecon Open. 2023 May;7(3):383-392. doi: 10.1007/s41669-023-00391-5. Epub 2023 Feb 9.
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.
本研究旨在比较不同外推建模技术的性能，并分析它们对癌症免疫治疗经济评估中结构不确定性的影响。
通过已发表的Checkmate 067 Kaplan Meier曲线重建个体患者数据。测试了标准参数模型和六种灵活技术，包括分数多项式、受限立方样条、Royston-Parmar模型、广义相加模型、参数混合模型和混合治愈模型。使用原始生存图的均方误差（MSE）和偏差来测试模型拟合度和外推性能。不同模型估计的增量成本效益比（ICER）的变异性用于反映经济评估中的结构不确定性。分别在3年和6.5年的截断值下分析和比较所有指标，以进一步讨论不同数据成熟度下的模型影响。可在GitHub上找到用于重现本研究的R代码。
无论数据成熟度如何，灵活技术通常比标准参数模型表现更好，MSE更小。使用未成熟数据进行长期外推预测的生存结果与成熟数据的结果不同。尽管在这种情况下没有找到表现最佳的模型，因为几个模型的MSE非常相似，但延长模拟周期时，建模的ICER的变异性显著增加。
在Checkmate 067的案例中，灵活技术表现更好，无论数据成熟度如何。模型选择会影响癌症免疫治疗的ICER，尤其是在处理未成熟生存数据时。当研究人员缺乏证据来确定“正确”的模型时，我们建议识别并揭示模型对结构不确定性的影响。
