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In sensitivity analysis, a sensitive variable is detected when changes in its input value result in considerable changes in the outcome [ 46].
Being the discount rate the most sensitive variable, we ran the model and performed probabilistic sensitivity analyses for different values.
The second part of the tool investigates the distribution of model errors for the different classes of a sensitive variable.
The tuning of a sensitive variable, in the context of estimating the population mean of a sensitive variable, is also introduced through an unsolved exercise.
This paper deals with the analysis of the estimation of the variance of a sensitive variable.
pH was determined as the most sensitive variable and was further optimized.
Similar(20)
In a second phase, we conducted a set of univariate sensitivity analyses using different sensitive variables each time.
One-way sensitivity analysis revealed that the most sensitive variables were probability of developing a DVT resulting from the insignificant difference in treatment efficacy.
The sensitive variables considered for the univariate sensitivity analyses were health care costs, (medical visits, hospitalization, nonpharmacological treatment, and drugs), QALY gained, and trial duration (from 6 to 12 months).
Developing sensitivity analysis (SA) that reliably and consistently identify sensitive variables can improve building performance design.
The first part examines the data for the hidden influence of user-defined sensitive variables on other variables.
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CEO of Professional Science Editing for Scientists @ prosciediting.com