Additionally, probabilistic sensitivity analysis was performed using Monte Carlo simulation to evaluate the multivariate uncertainty in the model, i.e., input parameters were varied simultaneously over specified ranges.
The tables for each mortality rate (neuropathy, nephropathy, CHD, LEA, and ESRD, and each concomitant disease state) are available in Additional File 1. Probabilistic sensitivity analysis was performed using Monte Carlo simulation to evaluate the multivariate uncertainty in the model.
To do this, we performed a multivariate uncertainty analysis by running the model with 1000 randomly sampled parameter values from the uncertainty distributions for the antiviral treatment SVR and the efficacy of OST and HCNSP on reducing HCV transmission risk (Table 1).
The model was assessed using Monte Carlo simulation to account for the multivariate uncertainty inherent in input parameters based on 10,000 bootstraps to test model and parameters.
This paper presents a model building strategy that consider the multivariate uncertainty as weighting matrix for the principal components.
A multivariate uncertainty and sensitivity analysis was performed to study the effects of input parameters on the control policy for the linear cost model.
Multivariate uncertainty and one-way sensitivity analyses were performed.
This was followed by a multivariate uncertainty analysis in which the simulation was repeated 10 000 times by Monte Carlo sampling from the uncertainty ranges around each parameter in the table and across the risk factor distributions listed in the appendix, generating 95% confidence intervals around the model's results.
Then we conducted multivariate uncertainty and sensitivity analysis to determine the uncertainty in the performance measure that was due to the uncertainty in estimating the input parameters.
In addition to parameter uncertainty, model uncertainty also exists.
