Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada.
Med Decis Making. 2024 Oct;44(7):787-801. doi: 10.1177/0272989X241264287. Epub 2024 Jul 31.
The expected value of sample information (EVSI) measures the expected benefits that could be obtained by collecting additional data. Estimating EVSI using the traditional nested Monte Carlo method is computationally expensive, but the recently developed Gaussian approximation (GA) approach can efficiently estimate EVSI across different sample sizes. However, the conventional GA may result in biased EVSI estimates if the decision models are highly nonlinear. This bias may lead to suboptimal study designs when GA is used to optimize the value of different studies. Therefore, we extend the conventional GA approach to improve its performance for nonlinear decision models.
Our method provides accurate EVSI estimates by approximating the conditional expectation of the benefit based on 2 steps. First, a Taylor series approximation is applied to estimate the conditional expectation of the benefit as a function of the conditional moments of the parameters of interest using a spline, which is fitted to the samples of the parameters and the corresponding benefits. Next, the conditional moments of parameters are approximated by the conventional GA and Fisher information. The proposed approach is applied to several data collection exercises involving non-Gaussian parameters and nonlinear decision models. Its performance is compared with the nested Monte Carlo method, the conventional GA approach, and the nonparametric regression-based method for EVSI calculation.
The proposed approach provides accurate EVSI estimates across different sample sizes when the parameters of interest are non-Gaussian and the decision models are nonlinear. The computational cost of the proposed method is similar to that of other novel methods.
The proposed approach can estimate EVSI across sample sizes accurately and efficiently, which may support researchers in determining an economically optimal study design using EVSI.
The Gaussian approximation method efficiently estimates the expected value of sample information (EVSI) for clinical trials with varying sample sizes, but it may introduce bias when health economic models have a nonlinear structure.We introduce the spline-based Taylor series approximation method and combine it with the original Gaussian approximation to correct the nonlinearity-induced bias in EVSI estimation.Our approach can provide more precise EVSI estimates for complex decision models without sacrificing computational efficiency, which can enhance the resource allocation strategies from the cost-effective perspective.
样本信息的预期价值（EVSI）衡量通过收集额外数据可获得的预期收益。使用传统的嵌套蒙特卡罗方法估计 EVSI 计算成本很高，但是最近开发的高斯逼近（GA）方法可以有效地在不同的样本量下估计 EVSI。但是，如果决策模型高度非线性，传统的 GA 可能会导致 EVSI 估计有偏差。当 GA 用于优化不同研究的价值时，这种偏差可能会导致次优的研究设计。因此，我们扩展了传统的 GA 方法，以提高其对非线性决策模型的性能。
我们的方法通过分两步来近似基于收益的条件期望来提供准确的 EVSI 估计。首先，应用泰勒级数逼近来估计条件期望作为感兴趣参数的条件矩的函数，使用样条对参数和相应的收益的样本进行拟合。接下来，使用传统的 GA 和 Fisher 信息来近似参数的条件矩。将所提出的方法应用于涉及非高斯参数和非线性决策模型的几个数据收集练习。将其性能与嵌套蒙特卡罗方法，传统的 GA 方法和基于非参数回归的 EVSI 计算方法进行比较。
当感兴趣的参数是非高斯的且决策模型是非线性的时，所提出的方法在不同的样本量下提供了准确的 EVSI 估计。所提出的方法的计算成本与其他新颖方法相似。
所提出的方法可以准确有效地估计样本量之间的 EVSI，这可以帮助研究人员使用 EVSI 确定经济上最优的研究设计。
GA 方法可有效地估计具有不同样本量的临床试验的预期值，但在健康经济模型具有非线性结构时可能会引入偏差。我们引入了基于样条的泰勒级数逼近方法，并将其与原始 GA 结合使用，以纠正 EVSI 估计中由非线性引起的偏差。我们的方法可以在不牺牲计算效率的情况下为复杂决策模型提供更精确的 EVSI 估计，从而可以从成本效益的角度增强资源分配策略。
