Health policy decisions are often informed by estimates of long-term survival based primarily on short-term data. A range of methods are available to include longer-term information, but there has previously been no comprehensive and accessible tool for implementing these. This paper introduces a novel model and software package for parametric survival modelling of individual-level, right-censored data, optionally combined with summary survival data on one or more time periods. It could be used to estimate long-term survival based on short-term data from a clinical trial, combined with longer-term disease registry or population data, or elicited judgements. All data sources are represented jointly in a Bayesian model. The hazard is modelled as an M-spline function, which can represent potential changes in the hazard trajectory at any time. Through Bayesian estimation, the model automatically adapts to fit the available data, and acknowledges uncertainty where the data are weak. Therefore long-term estimates are only confident if there are strong long-term data, and inferences do not rely on extrapolating parametric functions learned from short-term data. The effects of treatment or other explanatory variables can be estimated through proportional hazards or with a flexible non-proportional hazards model. Some commonly-used mechanisms for survival can also be assumed: cure models, additive hazards models with known background mortality, and models where the effect of a treatment wanes over time. All of these features are provided for the first time in an R package, $\texttt{survextrap}$, in which models can be fitted using standard R survival modelling syntax. This paper explains the model, and demonstrates the use of the package to fit a range of models to common forms of survival data used in health technology assessments.

翻译：卫生政策决策通常依赖于基于短期数据得出的长期生存估计。目前已有多种方法可纳入长期信息，但此前缺乏一个全面且易于使用的工具来实现这些方法。本文介绍了一种新颖的模型和软件包，用于对个体水平、右删失数据进行参数化生存建模，并可选择结合一个或多个时间段上的汇总生存数据。该方法可用于基于临床试验短期数据，结合长期疾病登记数据、人群数据或专家意见来估计长期生存。所有数据源通过贝叶斯模型联合表示。风险函数采用M样条函数建模，能够表示任意时间点上风险轨迹的潜在变化。通过贝叶斯估计，模型自动适应可用数据，并在数据较弱时体现不确定性。因此，长期估计仅在存在强长期数据时才具有可信度，且推断不依赖于从短期数据中推导的参数函数外推。治疗效果或其他解释变量的影响可通过比例风险模型或灵活的非比例风险模型进行估计。此外，还可假设一些常用的生存机制：治愈模型、具有已知背景死亡率的加性风险模型，以及治疗效果随时间减弱的模型。这些功能首次在R软件包$\texttt{survextrap}$中整合提供，用户可使用标准的R生存建模语法拟合模型。本文阐述了该模型，并通过将一系列模型应用于卫生技术评估中常见的生存数据类型，演示了该软件包的使用方法。