背景:样本信息的期望值(EVSI)衡量了通过收集其他数据可以获得的预期收益。使用传统的嵌套蒙特卡罗方法估计EVSI在计算上是昂贵的,但是最近开发的高斯近似(GA)方法可以有效地估计不同样本量的EVSI。然而,如果决策模型是高度非线性的,传统的遗传算法可能会导致有偏差的EVSI估计。当使用GA优化不同研究的价值时,这种偏差可能导致次优的研究设计。因此,我们扩展了传统的遗传算法,以提高其在非线性决策模型中的性能。
方法:我们的方法通过基于2个步骤近似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收益的条件期望,作为使用样条的感兴趣参数的条件矩的函数,对样本的参数和相应的收益进行拟合。接下来,参数的条件矩通过常规GA和Fisher信息近似。所提出的方法适用于涉及非高斯参数和非线性决策模型的多个数据收集练习。将其性能与嵌套蒙特卡罗方法进行了比较,传统的GA方法,以及基于非参数回归的EVSI计算方法。
结果:当感兴趣的参数为非高斯且决策模型为非线性时,所提出的方法可跨不同样本量提供准确的EVSI估计。所提出的方法的计算成本与其他新颖方法的计算成本相似。
结论:所提出的方法可以准确有效地估计跨样本量的EVSI,这可以支持研究人员使用EVSI确定经济上最佳的研究设计。
结论:高斯近似方法有效地估计了不同样本量的临床试验的样本信息(EVSI)的期望值,但是当健康经济模型具有非线性结构时,它可能会引入偏差。我们引入了基于样条的泰勒级数逼近方法,并将其与原始高斯逼近相结合,以校正EVSI估计中的非线性引起的偏差。我们的方法可以在不牺牲计算效率的情况下为复杂的决策模型提供更精确的EVSI估计,这可以从成本效益的角度增强资源分配策略。
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
CONCLUSIONS: 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.