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A sensitivity analysis for each random variable is also conducted.
A sensitivity analysis with the variables included showed no difference in outcomes.
In the second, we used the rctmiss command in STATA to conduct a missing not at random (MNAR) sensitivity analysis with the outcome variable in which the largest effect was observed (volume).
As last part of this study, sensitivity analysis involving all random variables present on the probabilistic problem was performed.
An efficient Pareto sensitivity analysis with respect to the design variables becomes possible with the proposed formulation.
An efficient sensitivity analysis with respect to the optimization variables and general system parameters becomes possible with the proposed formulation.
Missing data will be subjected to sensitivity analysis with respect to the outcome and exposure variables to determine whether it is reasonable to assume missingness at random.
We performed a sensitivity analysis with a reduced disease progression outcome variable to address this problem.
A sensitivity analysis with the FTE counts of the employee types was conducted and no added variables were significant.
We performed one-way sensitivity analyses for individual variables over their plausible ranges and propagated uncertainty in our model inputs using probabilistic sensitivity analysis with 10,000 Monte Carlo simulations.
The interesting problem is evaluation of the reliability index sensitivity with respect to random variables, mean value, and standard deviation.
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