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

Friday 8 May 2015, 12.30PM to 1.30pm

Speaker(s): Dr Mark Strong and Professor Alan Brennan, University of Sheffield

Abstract: Health economic models are used to estimate the expected net benefits of competing decision options. The true values of the input parameters of such models are rarely known with certainty, and it is often useful to quantify the value of undertaking further data collection in order to reduce uncertainty. An upper bound on the value of learning a subset of input parameters is quantified by its partial Expected Value of Perfect Information (EVPI). The value of a future study is quantified by its Expected Value of Sample Information (EVSI).

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”

The new methods allow Value of Information measures to be computed for models of any complexity, and hence be made more widely available to modellers and decision makers.

Location: ARRC Auditorium A/RC/014

Who to contact

For more information on these seminars, contact:
Alfredo Palacios
Shainur Premji

If you are not a member of University of York staff and are interested in attending a seminar, please contact 
so that we can ensure we have sufficient space

Economic evaluation seminar dates

  • Thursday 8 December
    Ana Duarte, University of York