Artificial neural network metamodel for sensitivity analysis in a total hip replacement health economic model

Objectives: Metamodels have been used to approximate complex simulations and have many applications with sensitivity analysis, optimization, etc. However, their use in health economics is very limited. Application of artificial neural network (ANN) with a health economic model has never been investigated. The study intends to introduce ANN as a metamodeling method to conduct sensitivity analysis in a total hip replacement decision analytical model and compare its performance with two other counterparts. Methods: First, a nonlinear factor screening method was adopted to screen out unimportant factors from the simulation. Second, an ANN was developed using the important variables to approximate the simulation. Performance of the ANN metamodel was then compared with its Gaussian Process (GP) and multiple linear regression (MLR) counterparts. Results: Out of 31, the factor screening method identified 12 important variables from the simulation. ANN metamodels showed best predictive capabilities in terms of performance measures (mean squared error of prediction, MSEP and mean absolute percentage deviation, MAPD) used for predicting both costs and quality-adjusted life years (QALYs) for two prostheses. Conclusion: The study provides a methodological development in sensitivity analysis and demonstrates that an ANN metamodel is a potential approximation method for computationally expensive health economic simulations.