Philips Zoe, Claxton Karl, Palmer Stephen
Centre for Health Economics, University of York, York, YO10 5DD [corrected] UK.
Med Decis Making. 2008 May-Jun;28(3):287-99. doi: 10.1177/0272989X07312724. Epub 2008 Apr 30.
To evaluate alternative approaches taken to estimate the population that could benefit from research and to demonstrate that explicitly modeling future change leads to more appropriate estimates of the expected value of information (EVI).
Existing approaches to estimating the population typically focus on the time horizon for decisions, employing seemingly arbitrary estimates of the appropriate horizon. These approaches implicitly use the time horizon as a proxy for future changes in technologies, prices, and information. Different approaches to quantifying the time horizon are explored, in the context of a stylized model, to demonstrate the impact of uncertainty in this estimate on EVI. An alternative approach is developed that explicitly models future changes in technologies, prices, and information and that demonstrates the impact on EVI estimates.
Explicitly modeling future changes means that the EVI for the decision problem may increase or decrease over time, but the EVI for the group of parameters that can be evaluated by current research tends to decline. The finite and infinite time horizons for the decision problem represent special cases (e.g., price shock or no changes, respectively). This type of analysis can be used to inform policy decisions relating to the timing of research.
The value of information depends on future changes in technologies, prices, and evidence. Finite time horizons for decision problems can be seen as a proxy for the complex and uncertain process of future change. A more explicit approach to modeling these changes could provide a more appropriate basis for calculating EVI, but this raises a number of significant methodological and technical challenges.
评估用于估计可能从研究中受益的人群的替代方法，并证明明确模拟未来变化会得出对信息期望值（EVI）更合适的估计。
现有的估计人群的方法通常侧重于决策的时间范围，采用对合适时间范围看似随意的估计。这些方法隐含地将时间范围用作技术、价格和信息未来变化的代理。在一个简化模型的背景下，探索了量化时间范围的不同方法，以证明该估计中的不确定性对EVI的影响。开发了一种替代方法，该方法明确模拟技术、价格和信息的未来变化，并展示其对EVI估计的影响。
明确模拟未来变化意味着决策问题的EVI可能随时间增加或减少，但当前研究可评估的参数组的EVI往往会下降。决策问题的有限和无限时间范围代表特殊情况（例如，分别为价格冲击或无变化）。这种类型的分析可用于为与研究时机相关的政策决策提供信息。
信息的价值取决于技术、价格和证据的未来变化。决策问题的有限时间范围可被视为未来变化这一复杂且不确定过程的代理。对这些变化进行建模的更明确方法可为计算EVI提供更合适的基础，但这带来了一些重大的方法和技术挑战。
