Objective: Our objective was to assess analytic decisions regarding model framework, structure, choice of parameters, and handling of uncertainty when modeling health co-benefits, and to make recommendations for improvements that could increase policy uptake.
Subsequent to the question of the most adequate model structure, the choice of optimal model order is addressed, as in particular the optimal parametrization of the noise model is not obvious.
In developing the QBM, when assumptions about model structure or choices about data sources were needed, we took the most conservative course, i.e. we chose to under-estimate the adverse health and health care cost consequences of smoking and to under-estimate the benefits of quitting.
This section presents the results of applying the statistical fusion model proposed in Section 3 to the Glasgow case study outlined in Section 2. Section 4.1 presents a validation study, which compares the appropriateness of our proposed model against a number of alternative models in terms of both model structure and covariate choice.
However, in most cases neither the model structure nor the choice of input and output variables is discretionary.
We reviewed several specific issues with modeling co-benefits of mitigation strategies, including those related to model framework, structure, and choice of parameters, and the implications of these for policy uptake.
Choices concerning model structure and covariate selection were based on the reduction in NONMEM objective function value and interindividual variability, together with the examination of residual plots.
The choice of hydrologic model structure and complexity, and the choice of emissions scenario, has been investigated and somewhat resolved; however, recharge results remain sensitive to downscaling methods.
This observation further supports the ongoing PTTMAM development towards a cross-nested choice model structure.
