The half-life of truth: what are appropriate time horizons for research decisions?
OBJECTIVE
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).


METHODS
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.


RESULTS
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.


CONCLUSIONS
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.
Philips
Zoe
Z
Centre for Health Economics, University of York, York, YO10 5DD [corrected] UK. che-teehta@york.ac.uk [corrected]
Claxton
Karl
K
Palmer
Stephen
S
D003657:Decision Making; D015233:Models, Statistical; D012106:Research / Q000706:statistics & numerical data; D012107:Research Design; D013997:Time Factors