Warwick Medical School, University of Warwick, CV4 7HL, Coventry, UK.
BMC Med Inform Decis Mak. 2024 Mar 14;24(1):76. doi: 10.1186/s12911-024-02475-6.
Economic evaluation of emerging health technologies is mandated by agencies such as the National Institute for Health and Care Excellence (NICE) to ensure their cost is proportional to their benefit. To avoid bias, NICE stipulate that the benefit of a treatment is assessed across the lifetime of the patient population, which can be many decades. Unfortunately, follow-up from a clinical trial will not usually cover the required period and the observed follow-up will require extrapolation. For survival data this is often done by selecting a preferred model from a set of candidate parametric models. This approach is limited in that the choice of model is restricted to those originally fitted. What if none of the models are consistent with clinical prediction or external data?
METHOD/RESULTS: This paper introduces SurvInt, a tool that estimates the parameters of common parametric survival models which interpolate key survival time co-ordinates specified by the user, which could come from external trials, real world data or expert clinical opinion. This is achieved by solving simultaneous equations based on the survival functions of the parametric models. The application of SurvInt is shown through two examples where traditional parametric modelling did not produce models that were consistent with external data or clinical opinion. Additional features include model averaging, mixture cure models, background mortality, piecewise modelling, restricted mean survival time estimation and probabilistic sensitivity analysis.
SurvInt allows precise parametric survival models to be estimated and carried forward into economic models. It provides access to extrapolations that are consistent with multiple data sources such as observed data and clinical predictions, opening the door to precise exploration of regions of uncertainty/disagreement. SurvInt could avoid the need for post-hoc adjustments for complications such as treatment switching, which are often applied to obtain a plausible survival model but at the cost of introducing additional uncertainty. Phase III clinical trials are not designed with extrapolation in mind, and so it is sensible to consider alternative approaches to predict future survival that incorporate external information.
新兴卫生技术的经济评估是国家卫生与保健卓越研究所（NICE）等机构的要求，以确保其成本与其效益相称。为避免偏差，NICE 规定，治疗的效益应在患者人群的一生中进行评估，这可能需要几十年的时间。不幸的是，临床试验的随访通常不会涵盖所需的时间，并且需要对观察到的随访进行外推。对于生存数据，通常通过从一组候选参数模型中选择首选模型来完成。这种方法的局限性在于，模型的选择仅限于最初拟合的模型。如果没有一个模型与临床预测或外部数据一致呢？
方法/结果：本文介绍了 SurvInt，这是一种工具，可以估计常见参数生存模型的参数，这些模型内插用户指定的关键生存时间坐标，这些坐标可以来自外部试验、真实世界数据或专家临床意见。这是通过基于参数生存函数解联立方程来实现的。通过两个例子展示了 SurvInt 的应用，在这两个例子中，传统的参数建模没有产生与外部数据或临床意见一致的模型。附加功能包括模型平均、混合治愈模型、背景死亡率、分段建模、限制平均生存时间估计和概率敏感性分析。
SurvInt 允许精确的参数生存模型被估计并推进到经济模型中。它提供了对与多个数据源一致的外推的访问，例如观察数据和临床预测，为精确探索不确定性/分歧区域打开了大门。SurvInt 可以避免为了获得合理的生存模型而进行事后调整，例如治疗转换，这通常会引入额外的不确定性。III 期临床试验并非专门设计用于外推，因此考虑采用包含外部信息的替代方法来预测未来的生存是明智的。
