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The proposed approach, therefore, aims at providing settings to model control parameters at which a certain model outcome is best in performance and is less sensitive to variations in model random (noise) factors.
Results of the one-way sensitivity analyses show that the model outcome is highly sensitive to variations in interventional treatment prices (inpatient as well as outpatient) and the proportion of outpatient treatments.
Decision analytical modeling techniques as well as qualitative methods, such as belief elicitation method and scenario drafting, were applied, and parameters for which the model outcome is most sensitive was explored.
The model outcome is very sensitive to the cost of isolation.
This suggests that model outcome is clinically reliable and model errors are sufficiently small.
Each model outcome is presented with a point estimate along with uncertainty reflected by the 2.5 th and 97.5th percentile of the uncertainty distribution.
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The model outcome was the incremental cost-effectiveness ratio which was assessed for various threshold values (€0 - €70,000 per asthma case avoided).
19 The model outcome was relapse-related hospitalizations avoided, and the per patient mean value was estimated over the 5-year time horizon.
The impact of uncertain input values on model outcomes is estimated using the Info-Gap decision analysis framework.
Assuming the 1% standard error, this economic model result is -1% +/- 3% or the range of the possible model outcomes is between -4% to +2%.
The key difference between the two model outcomes is polyethylene wear.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com