Ades A E, Lu G, Claxton K
Medical Research Council Health Services Research Collaboration, Bristol, United Kingdom.
Med Decis Making. 2004 Mar-Apr;24(2):207-27. doi: 10.1177/0272989X04263162.
There has been an increasing interest in using expected value of information (EVI) theory in medical decision making, to identify the need for further research to reduce uncertainty in decision and as a tool for sensitivity analysis. Expected value of sample information (EVSI) has been proposed for determination of optimum sample size and allocation rates in randomized clinical trials. This article derives simple Monte Carlo, or nested Monte Carlo, methods that extend the use of EVSI calculations to medical decision applications with multiple sources of uncertainty, with particular attention to the form in which epidemiological data and research findings are structured. In particular, information on key decision parameters such as treatment efficacy are invariably available on measures of relative efficacy such as risk differences or odds ratios, but not on model parameters themselves. In addition, estimates of model parameters and of relative effect measures in the literature may be heterogeneous, reflecting additional sources of variation besides statistical sampling error. The authors describe Monte Carlo procedures for calculating EVSI for probability, rate, or continuous variable parameters in multi parameter decision models and approximate methods for relative measures such as risk differences, odds ratios, risk ratios, and hazard ratios. Where prior evidence is based on a random effects meta-analysis, the authors describe different ESVI calculations, one relevant for decisions concerning a specific patient group and the other for decisions concerning the entire population of patient groups. They also consider EVSI methods for new studies intended to update information on both baseline treatment efficacy and the relative efficacy of 2 treatments. Although there are restrictions regarding models with prior correlation between parameters, these methods can be applied to the majority of probabilistic decision models. Illustrative worked examples of EVSI calculations are given in an appendix.
在医学决策中，人们越来越关注使用信息期望值（EVI）理论，以确定是否需要进一步研究来减少决策中的不确定性，并将其作为敏感性分析的工具。样本信息期望值（EVSI）已被提议用于确定随机临床试验中的最佳样本量和分配率。本文推导了简单的蒙特卡洛方法或嵌套蒙特卡洛方法，将EVSI计算的应用扩展到具有多个不确定性来源的医学决策应用中，特别关注流行病学数据和研究结果的结构形式。具体而言，关于关键决策参数（如治疗效果）的信息通常以相对疗效的度量（如风险差异或比值比）提供，而不是关于模型参数本身。此外，文献中模型参数和相对效应度量的估计可能存在异质性，这反映了除统计抽样误差之外的其他变异来源。作者描述了在多参数决策模型中计算概率、率或连续变量参数的EVSI的蒙特卡洛程序，以及风险差异、比值比、风险比和风险比等相对度量的近似方法。如果先前的证据基于随机效应荟萃分析，作者描述了不同的ESVI计算方法，一种适用于针对特定患者群体的决策，另一种适用于针对整个患者群体的决策。他们还考虑了用于新研究的EVSI方法，这些新研究旨在更新关于基线治疗效果和两种治疗相对疗效的信息。尽管对于参数之间存在先验相关性的模型存在限制，但这些方法可以应用于大多数概率决策模型。附录中给出了EVSI计算的说明性实例。
