Department of Health Management and Health Economics, University of Oslo, Oslo, Norway.
Norwegian Medicines Agency, Oslo, Norway.
Med Decis Making. 2022 Jul;42(5):612-625. doi: 10.1177/0272989X211068019. Epub 2021 Dec 30.
Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we develop new methods for computing the EVSI of extending an existing trial's follow-up, first for an assumed survival model and then extending to capture uncertainty about the true survival model.
We developed a nested Markov Chain Monte Carlo procedure and a nonparametric regression-based method. We compared the methods by computing single-model and model-averaged EVSI for collecting additional follow-up data in 2 synthetic case studies.
There was good agreement between the 2 methods. The regression-based method was fast and straightforward to implement, and scales easily to include any number of candidate survival models in the model uncertainty case. The nested Monte Carlo procedure, on the other hand, was extremely computationally demanding when we included model uncertainty.
We present a straightforward regression-based method for computing the EVSI of extending an existing trial's follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. EVSI for ongoing trials can help decision makers determine whether early patient access to a new technology can be justified on the basis of the current evidence or whether more mature evidence is needed.
Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life-expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial.In this article, we have developed new methods for computing the EVSI of extending a trial's follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. We extend a previously described nonparametric regression-based method for computing EVSI, which we demonstrate in synthetic case studies is fast, straightforward to implement, and scales easily to include any number of candidate survival models in the EVSI calculations.The EVSI methods that we present in this article can quantify the need for collecting additional follow-up data before making an adoption decision given any decision-making context.
在临床试验仍处于早期阶段时，就越来越多地需要对新的医疗技术做出决策，这可能会导致关键决策驱动因素（如预期寿命和疾病进展时间的估计）存在很大的不确定性。额外的数据收集可以减少不确定性，其价值可以通过计算样本信息的预期价值（EVSI）来量化，这通常是在设计未来试验的背景下描述的。在本文中，我们开发了新的方法来计算扩展现有试验随访的 EVSI，首先是针对假设的生存模型，然后扩展到捕捉对真实生存模型的不确定性。
我们开发了嵌套的马尔可夫链蒙特卡罗程序和基于非参数回归的方法。我们通过在两个合成案例研究中计算收集额外随访数据的单模型和模型平均 EVSI，比较了这些方法。
两种方法之间具有很好的一致性。基于回归的方法快速且易于实现，并且在模型不确定性的情况下，很容易扩展到包括任意数量的候选生存模型。另一方面，当我们包括模型不确定性时，嵌套的蒙特卡罗过程的计算要求非常高。
我们提出了一种简单的基于回归的方法来计算扩展现有试验随访的 EVSI，包括假设单个已知生存模型和对真实生存模型不确定的情况。正在进行的试验的 EVSI 可以帮助决策者确定是否可以根据当前证据提前让新的治疗方法惠及患者，或者是否需要更成熟的证据。
在临床试验仍处于早期阶段时，就越来越多地需要对新的医疗技术做出决策，这可能会导致关键决策驱动因素（如预期寿命和疾病进展时间的估计）存在很大的不确定性。额外的数据收集可以减少不确定性，其价值可以通过计算样本信息的预期价值（EVSI）来量化，这通常是在设计未来试验的背景下描述的。在本文中，我们开发了新的方法来计算扩展试验随访的 EVSI，包括假设单个已知生存模型和对真实生存模型不确定的情况。我们扩展了之前描述的基于非参数回归的 EVSI 计算方法，该方法在合成案例研究中快速、易于实现，并且很容易扩展到包括 EVSI 计算中的任意数量的候选生存模型。本文提出的 EVSI 方法可以在任何决策背景下，量化在做出采用决策之前收集额外随访数据的需求。
