Menzies Nicolas A
Department of Global Health and Population and the Center for Health Decision Science, Harvard University, Boston, MA (NAM)
Med Decis Making. 2016 Apr;36(3):308-20. doi: 10.1177/0272989X15583495. Epub 2015 Apr 24.
Conventional estimators for the expected value of sample information (EVSI) are computationally expensive or limited to specific analytic scenarios. I describe a novel approach that allows efficient EVSI computation for a wide range of study designs and is applicable to models of arbitrary complexity.
The posterior parameter distribution produced by a hypothetical study is estimated by reweighting existing draws from the prior distribution. EVSI can then be estimated using a conventional probabilistic sensitivity analysis, with no further model evaluations and with a simple sequence of calculations (Algorithm 1). A refinement to this approach (Algorithm 2) uses smoothing techniques to improve accuracy. Algorithm performance was compared with the conventional EVSI estimator (2-level Monte Carlo integration) and an alternative developed by Brennan and Kharroubi (BK), in a cost-effectiveness case study.
Compared with the conventional estimator, Algorithm 2 exhibited a root mean square error (RMSE) 8%-17% lower, with far fewer model evaluations (3-4 orders of magnitude). Algorithm 1 produced results similar to those of the conventional estimator when study evidence was weak but underestimated EVSI when study evidence was strong. Compared with the BK estimator, the proposed algorithms reduced RSME by 18%-38% in most analytic scenarios, with 40 times fewer model evaluations. Algorithm 1 performed poorly in the context of strong study evidence. All methods were sensitive to the number of samples in the outer loop of the simulation.
The proposed algorithms remove two major challenges for estimating EVSI--the difficulty of estimating the posterior parameter distribution given hypothetical study data and the need for many model evaluations to obtain stable and unbiased results. These approaches make EVSI estimation feasible for a wide range of analytic scenarios.
用于样本信息期望值（EVSI）的传统估计方法计算成本高昂，或仅限于特定的分析场景。我描述了一种新颖的方法，该方法能对广泛的研究设计进行高效的EVSI计算，并且适用于任意复杂程度的模型。
通过对来自先验分布的现有抽样进行重新加权，估计假设性研究产生的后验参数分布。然后，可以使用传统的概率敏感性分析来估计EVSI，无需进一步的模型评估，且只需简单的一系列计算（算法1）。对该方法的一种改进（算法2）使用平滑技术来提高准确性。在一个成本效益案例研究中，将算法性能与传统的EVSI估计器（两级蒙特卡罗积分）以及由布伦南和哈尔鲁比（BK）开发的另一种方法进行了比较。
与传统估计器相比，算法2的均方根误差（RMSE）低8%-17%，模型评估次数要少得多（少3-4个数量级）。当研究证据较弱时，算法1产生的结果与传统估计器相似，但当研究证据较强时，算法1低估了EVSI。与BK估计器相比，在大多数分析场景中，所提出的算法将RMSE降低了18%-38%，模型评估次数减少了40倍。在研究证据较强的情况下，算法1的表现较差。所有方法对模拟外循环中的样本数量都很敏感。
所提出的算法消除了估计EVSI的两个主要挑战——给定假设性研究数据时估计后验参数分布的困难，以及为获得稳定且无偏结果而需要进行大量模型评估的问题。这些方法使EVSI估计在广泛的分析场景中变得可行。
