Then, to understand how uncertainty on parameters would affect uncertainty on the results obtained for each strategy described in table 3, we performed multivariate uncertainty and sensitivity analyses including all parameters except those defining each strategy (detection and cure rates).
Multivariate uncertainty and one-way sensitivity analyses were performed.
Probabilistic sensitivity analyses (PSAs) were used to assess the influence of multivariate uncertainty on the incremental cost-effectiveness ratios.
Appropriate variability and uncertainty analyses were conducted.
This technique is called random error analyses or uncertainty analyses.
Uncertainty in the analysis will be explored using a combination of univariate and multivariate analyses, and decision uncertainty will be addressed using cost-effectiveness acceptability curves.
Uncertainty surrounding outcomes was addressed via multivariate sensitivity analyses.
To explore uncertainty in costs and outcomes, we performed univariate and multivariate sensitivity analyses.
The impact of parameter uncertainty should always be assessed using deterministic univariate or multivariate sensitivity analyses (3, 27– 27).
Multivariate sensitivity analyses were used to explore the implications of parameter uncertainty, including wide sampling ranges around the SVR estimates to account for settings with different SVR rates or genotype distributions.
