背景:在卫生技术评估中,限制平均生存时间和预期寿命通常被评估。参数模型通常用于外推。使用相对生存框架的样条模型已被证明可以更可靠地估计癌症患者的预期寿命;然而,需要更多的研究来评估使用全因生存框架的样条模型和使用相对生存框架的标准参数模型.
目的:在相对生存和全因生存框架内使用标准参数模型和样条模型评估生存外推。
方法:来自瑞典癌症登记处,我们确定了被诊断患有5种癌症的患者(结肠癌,乳房,黑色素瘤,前列腺,和慢性髓性白血病)在1981年至1990年之间,随访至2020年。根据癌症和年龄组(18-59、60-69和70-99岁)将患者分为15个癌症队列。我们在2、3、5和10y对随访进行了正确审查,并在全因和相对生存框架内拟合了参数模型,以与观察到的Kaplan-Meier生存估计相比,推断为10y和寿命。所有队列均使用6个标准参数模型(指数,威布尔,Gompertz,日志-逻辑,log-normal,和广义伽马)和3样条模型(关于危险,赔率,和正常尺度)。
结果:对于预测10年生存率,样条模型通常比标准参数模型表现更好。然而,使用全因或相对生存框架未显示任何明显差异.为了终身生存,从相对生存框架推断与观察到的生存更一致,特别是使用样条模型。
结论:对于外推至10年,我们推荐样条模型。为了推断寿命,我们建议在相对生存框架中推断,特别是使用样条模型。
结论:对于生存外推至10年,样条模型通常比标准参数模型表现更好。然而,使用全因或相对生存框架,在相同的参数模型下没有明显差异.在相对生存框架内生存外推至寿命与观察到的数据非常吻合。特别是使用样条模型。在全因生存框架内推断参数模型可能会高估一生的生存比例;相对生存方法的模型可能会低估。
BACKGROUND: In health technology assessment, restricted mean survival time and life expectancy are commonly evaluated. Parametric models are typically used for extrapolation. Spline models using a relative survival framework have been shown to estimate life expectancy of cancer patients more reliably; however, more research is needed to assess spline models using an all-cause survival framework and standard parametric models using a relative survival framework.
OBJECTIVE: To assess survival extrapolation using standard parametric models and spline models within relative survival and all-cause survival frameworks.
METHODS: From the Swedish Cancer Registry, we identified patients diagnosed with 5 types of cancer (colon, breast, melanoma, prostate, and chronic myeloid leukemia) between 1981 and 1990 with follow-up until 2020. Patients were categorized into 15 cancer cohorts by cancer and age group (18-59, 60-69, and 70-99 y). We right-censored the follow-up at 2, 3, 5, and 10 y and fitted the parametric models within an all-cause and a relative survival framework to extrapolate to 10 y and lifetime in comparison with the observed Kaplan-Meier survival estimates. All cohorts were modeled with 6 standard parametric models (exponential, Weibull, Gompertz, log-logistic, log-normal, and generalized gamma) and 3 spline models (on hazard, odds, and normal scales).
RESULTS: For predicting 10-y survival, spline models generally performed better than standard parametric models. However, using an all-cause or a relative survival framework did not show any distinct difference. For lifetime survival, extrapolating from a relative survival framework agreed better with the observed survival, particularly using spline models.
CONCLUSIONS: For extrapolation to 10 y, we recommend spline models. For extrapolation to lifetime, we suggest extrapolating in a relative survival framework, especially using spline models.
CONCLUSIONS: For survival extrapolation to 10 y, spline models generally performed better than standard parametric models did. However, using an all-cause or a relative survival framework showed no distinct difference under the same parametric model.Survival extrapolation to lifetime within a relative survival framework agreed well with the observed data, especially using spline models.Extrapolating parametric models within an all-cause survival framework may overestimate survival proportions at lifetime; models for the relative survival approach may underestimate instead.