考虑缺失数据计算样本信息的期望值
Calculating the Expected Value of Sample Information accounting for missing data
AI总结：
研究在收集的额外证据存在缺失数据时计算样本信息期望值（EVSI）的方法，通过模拟数据、引入缺失机制、应用多重插补和非参数回归计算EVSI，应用于健康经济模型，发现校正缺失需更多样本量，或为现实研究样本量计算提供新技术。
AI中文摘要：
样本信息的期望值（EVSI）是确定用于经济模型的额外证据价值的有力工具。然而，EVSI仅应用于理想化的数据收集机制，限制了其在现实研究中的潜在应用。本文定义了一种方法，用于在目标收集的额外证据存在缺失数据时计算EVSI。首先定义如何模拟个体层面数据并在模拟数据中引入缺失性，重现完全随机缺失（MCAR）、随机缺失（MAR）和非随机缺失（MNAR）机制。然后应用多重插补方法调整与缺失数据相关的偏差。最后用插补后的数据通过非参数回归方法计算EVSI。将新方法应用于两个不同的健康经济模型，比较不同缺失数据类型下计算的EVSI。结果表明，当额外证据存在缺失时EVSI会降低，还定义了一种方法来有效计算恢复理想化EVSI所需收集的样本量。发现校正缺失数据所需的额外样本数量超过标准方法。该方法能在更现实场景中计算EVSI，恢复理想化EVSI所需的必要样本量超过标准方法，这可能代表了现实研究中样本量计算的新技术。
英文摘要：
The Expected Value of Sample Information (EVSI) is a powerful instrument to determine the value of additional evidence to inform an economic model. However, EVSI has been applied only to idealized data collection mechanisms, thereby reducing its potential applications in realistic studies. In this paper, we define a methodology to calculate EVSI when the additional evidence we aim to collect exhibits missing data; a very common challenge in real-world studies. First, we define how to simulate individual-level data and how to induce missingness inside the simulated data. We will reproduce Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR) missing data mechanisms. Then, we will apply the multiple imputation method to adjust for the bias related to the missing data. Finally, we use the imputed data to compute the EVSI using nonparametric regression methods. We apply the novel methodology to two different health economic models and compare the EVSI computed on data without missingness with the EVSI with different types of missing data. We show that the EVSI decreases when the additional evidence suffers from missingness, and therefore, we define a method to efficiently compute the sample size we need to collect to recover the idealized EVSI (without missingness). We find out that the number of additional samples needed to correct for missing data exceeds that coming from standard approaches. With this methodology, we compute EVSI when the additional data are affected by non-trivial forms of missingness, modeling both the MAR and MNAR mechanisms, and extending EVSI calculation to more realistic scenarios. The fact that the necessary sample size to recover the idealized EVSI exceeds that of standard methods suggests that this methodology potentially represents a novel technique for sample size calculation in realistic studies.
