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In addition, the simulation result of the process model is applied to analyse the value of retrofitting flexibility and the economic-viable chance of retrofitting the underlying project through a real option analysis model.
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Uncertainty in real option analysis can be modelled as nonstationary stochastic processes, where the distribution may change over time.
Functional enrichment results obtained from separate batches are integrated in a similar fashion (Dataset option C for each analysis model).
Two distinct real options analysis (ROA) models are developed to evaluate and optimize flexible strategies for these systems, relying on a novel methodological approach to value flexibility based on decision rules.
Whether we term it option analysis or robust optimisation or Bayesian analysis or just stochastic analysis, an assessment is surely needed of the impact of all known systematic risks and uncertainties on a comprehensive, integrated model.
It requires monitoring, fieldwork, analysis, model building and communication.
For the search of finding the optimal analysis strategy, we have applied 10 different analysis models on different data selection options.
Decision analysis models can assist the physician in accounting for these factors and customize patient counseling with regard to treatment options.
One of the advantages of decision analysis modeling is the ability to use sensitivity analysis to determine threshold values for critical input variables (e.g., age, risk of complications, cost) which influence the comparative effectiveness of each treatment option.
With the time varying treatment effects obtained from the network meta-analysis models presented in this paper there are different options to create probabilistic summaries of treatment effects.
Changes in the total units of option categories in Model B and total ELS costs of Model A were negligible (<5 %) compared to the weighted PHB analysis.
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