Purpose: Decision makers require evidence on cost-effectiveness when making decisions to reimburse any particular health technology. There may be a great deal of uncertainty with cost and effectiveness data, hence health economic evaluations should also investigate whether collecting additional information may reduce this uncertainty. Value of Information (VoI) methods seek to quantify the existing level of uncertainty in terms of the expected net benefit associated with obtaining perfect information on all model parameters. VoI can be computed using Monte Carlo methods which involve a large number of model evaluations. Practical problems lie in the computational requirements of performing VoI analysis for structurally complex models with large number of factors. This paper demonstrates a new methodological framework for expected value of information analysis from complex health economics models.
Method: Linear regression (LR) and Gaussian process (GP) metamodels have recently been used in a number of cost effectiveness studies but both have limitations. LR assumes linearity which severely limits its applicability while the available software for GP restricts the number of model input parameters to 30. This work-in-progress study introduces a framework, which involves running a factor screening method, based on statistical experimental design approach, to role out unimportant factors from the simulation and then developing a new non-parametric function approximation method, called artificial neural network (ANN) as a simulation metamodel. The framework was explored previously by the first-author in a non-health economics application, which does not require any specific input-output functional relationship and can handle any number of input parameters. The paper illustrates the method through an EVPI analysis from a case study of Total Hip Replacement and reports on the preliminary results.
Result: The factor screening method was able to detect 12 important factors, out of 31, from the Total Hip Replacement model. Using 12 important factors, an ANN simulation metamodel was developed to predict per patient EVPI. The estimated per patient EVPI was then compared with those calculated from the linear regression and Gaussian process metamodels. From preliminary results, an ANN metamodel has showed better predictive capability than its LR and GP counterparts in EVPI analysis.
Conclusion: An ANN metamodel, along with a factor screening method, has a great potential in VoI analysis from a large and complex health economic model.
Candidate for the Lee B. Lusted Student Prize Competition