Study Uncertainty with Sensitivity Analysis
See how input uncertainty impacts your model results and conclusions
TreeAge Pro’s built-in sensitivity analysis tools make it easy
When presenting model results, it is essential to consider the impact of uncertainty in model inputs. Sensitivity analysis runs the model many times with different values for model inputs, so that a full range of possible model outcomes can be considered.
There are two distinct forms of sensitivity analysis.
- Deterministic Sensitivity Analysis – use a range for each uncertain model input with each input analyzed separately.
- Probabilistic Sensitivity Analysis – sample from a distribution of possible model input values with all uncertain inputs analyzed together.
Deterministic Sensitivity Analysis
Examine the impact of individual parameters on your model.
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Probabilistic Sensitivity Analysis
Assess confidence in your conclusions by studying the impact combined parameter uncertainty.
- Distributions: Create distributions to represent uncertainty related to specific inputs. This prevents over-representation of outlier input values.
- PSA Analysis: Sample a set input values from distributions, then run the model using those samples. Repeat this many times to generate a large set of results representing a wide range or parameter combinations.
- Interpretation: Examine the set of model analysis results to assess confidence in the base case model conclusions. Some individual model calculations within the PSA will confirm your conclusions – the higher the percentage, the more confidence we have.
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