Fast efficient computation of value of information from a probabilistic sensitivity analysis sample: a non-parametric regression approach

The standard approach to computing both partial EVPI and EVSI is via a nested two-level Monte Carlo scheme that includes at each inner loop step both parameter sampling and model evaluation. This scheme can be prohibitively slow for complex models. Additional problems arise if the two-level Monte Carlo scheme results in an inner loop conditional distribution that is difficult to sample from. This most commonly occurs when computing EVSI for a problem in which the parameter distribution is not conjugate to the data likelihood, but can also occur when computing partial EVPI where parameters are correlated. In either case we typically need to resort to MCMC methods, implemented for example in WinBUGS. In practice, these difficulties have resulted in the restriction of Value of Information analyses to only a small subset of health economic evaluation studies.

To overcome the problems above we have developed fast and efficient non-parametric regression based methods for computing partial EVPI and EVSI. The methods require only the "probabilistic sensitivity analysis" (PSA) sample: a single set of samples from the model parameters, along with the corresponding model evaluations. We have developed an easy to use web-based Value of Information calculator called “SAVI” http://savi.shef.ac.uk/SAVI.