The mean survival is the key ingredient of the decision process in several applications, notably in health economic evaluations. It is defined as the area under the complete survival curve, thus necessitating extrapolation of the observed data. This may be achieved in a more stable manner by borrowing long term evidence from registry and demographic data. Such borrowing can be seen as an implicit bias-variance trade-off in unseen data. In this article we employ a Bayesian mortality model and transfer its projections in order to construct the baseline population that acts as an anchor of the survival model. We then propose extrapolation methods based on flexible parametric polyhazard models which can naturally accommodate diverse shapes, including non-proportional hazards and crossing survival curves, while typically maintaining a natural interpretation. We estimate the mean survival and related estimands in three cases, namely breast cancer, cardiac arrhythmia and advanced melanoma. Specifically, we evaluate the survival disadvantage of triple-negative breast cancer cases, the efficacy of combining immunotherapy with mRNA cancer therapeutics for melanoma treatment and the suitability of implantable cardioverter defibrilators for cardiac arrhythmia. The latter is conducted in a competing risks context illustrating how working on the cause-specific hazard alone minimizes potential instability. The results suggest that the proposed approach offers a flexible, interpretable and robust approach when survival extrapolation is required.

翻译：平均生存时间是多个应用领域（尤其是卫生经济学评估）决策过程中的关键要素。其定义为完整生存曲线下的面积，因此需要对观测数据进行外推。通过从登记数据和人口统计数据中借鉴长期证据，可以更稳定地实现这一目标。此类借鉴可视为对未见数据中隐含偏差-方差权衡的处理。本文采用贝叶斯死亡率模型并迁移其预测结果，以构建作为生存模型锚点的基线人群。随后提出基于灵活参数化多风险模型的生存外推方法，该方法能自然适应包括非比例风险和交叉生存曲线在内的多种形态，同时通常保持直观的可解释性。我们在三种临床案例（乳腺癌、心律失常和晚期黑色素瘤）中估计平均生存时间及相关参数。具体而言，我们评估了三阴性乳腺癌病例的生存劣势、免疫疗法联合mRNA癌症药物治疗黑色素瘤的疗效，以及植入式心脏复律除颤器治疗心律失常的适用性。最后一项研究在竞争风险框架下展开，说明仅针对病因特异性风险进行分析可最大程度降低潜在的不稳定性。结果表明，当需要进行生存外推时，所提出的方法能提供灵活、可解释且稳健的解决方案。