Sensitivity Analysis with Event Tree Analysis
Sensitivity Analysis Integrated with Event Tree Analysis · Also known as: SA-ETA, ETA sensitivity analysis, event tree sensitivity analysis, probabilistic sensitivity analysis with ETA
Sensitivity analysis with event tree analysis (SA-ETA) is a quantitative risk assessment approach that systematically varies the input probabilities of an event tree model to determine which branch probabilities or initiating event frequencies most strongly influence the calculated probability of undesired outcomes. It extends classical event tree analysis by ranking the uncertainty contributions of individual inputs, thereby guiding risk-reduction efforts toward the parameters that matter most.
Read the full method
Sign in with a free account to read this section.
Method map
The neighbourhood of related methods — select a node to explore.
When to use it
Use SA-ETA when you have a quantitative event tree model and need to understand which input probabilities drive risk, especially when input data are uncertain or derived from expert judgment. It is appropriate in nuclear, chemical, aerospace, and process industries where regulatory risk targets must be met and uncertainty communication is required. Do not use when the event tree has not been built on defensible probability data — sensitivity analysis amplifies, not compensates for, poorly grounded base estimates. Also avoid when a purely qualitative risk screen is sufficient and resources for quantitative analysis are not available.
Strengths & limitations
- Identifies which barrier probabilities have the greatest leverage on accident risk, enabling targeted risk reduction.
- Supports transparent communication of uncertainty to regulators and decision-makers.
- Compatible with both simple OAT perturbation and rigorous global methods (Monte Carlo, Sobol) depending on resource availability.
- Can integrate with fault tree sub-models for a fully quantified probabilistic risk assessment.
- Helps prioritize data collection and research investment toward the most uncertainty-sensitive parameters.
- Results are only as reliable as the event tree structure and the base probability estimates; garbage-in, garbage-out applies.
- Global sensitivity analysis (Monte Carlo) requires many model evaluations and can be computationally intensive for large, complex trees.
- OAT sensitivity does not capture interaction effects between inputs — two low-ranked inputs may jointly dominate if they are correlated.
- Assigning defensible uncertainty ranges for branch probabilities often requires expert elicitation, which is itself uncertain and resource-intensive.
Frequently asked
What is the difference between OAT sensitivity and global sensitivity analysis?
One-at-a-time (OAT) sensitivity varies each input individually while holding others fixed, measuring local influence at the base-case values. Global sensitivity analysis (e.g., Sobol indices, Morris screening) samples all inputs simultaneously across their full uncertainty ranges and measures how much of the total output variance is attributable to each input and to interactions between inputs. OAT is simpler and faster; global methods are more informative but computationally demanding.
Can I apply sensitivity analysis if my branch probabilities come from expert judgment rather than data?
Yes, and in fact this is one of the strongest use cases. When probabilities are expert-elicited they carry wide uncertainty ranges, and sensitivity analysis reveals which judgments most influence the conclusion. If a critical input is entirely expert-driven and highly sensitive, it flags a priority area for additional data collection or physical testing.
Does SA-ETA replace uncertainty analysis?
No — they are complementary. Uncertainty analysis propagates input distributions through the model to characterize the distribution of the output (e.g., a 5th–95th percentile range for end-state frequency). Sensitivity analysis explains how much each input contributes to that output uncertainty. Both are typically reported together in quantitative risk assessments.
How many model evaluations does a global sensitivity analysis require?
Sobol-based methods typically require N(k+2) evaluations, where N is a base sample size (often 500–10,000) and k is the number of uncertain inputs. For a tree with 20 uncertain branches this can mean tens of thousands of evaluations. Morris screening requires far fewer (r(k+1) runs, commonly 50–200 total) and is a practical first screen before committing to full Sobol analysis.
Should every event tree analysis include sensitivity analysis?
Not necessarily. A purely qualitative or screening-level ETA aims to identify scenario logic, not quantify probabilities precisely; sensitivity analysis adds little in that context. Sensitivity analysis is most valuable when the ETA is quantified, probability inputs are uncertain, and decisions (regulatory compliance, capital allocation for safety improvements) depend on the risk estimates.
Sources
- Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 978-0470059975
- Event tree analysis. Wikipedia. link ↗
How to cite this page
ScholarGate. (2026, June 3). Sensitivity Analysis Integrated with Event Tree Analysis. ScholarGate. https://scholargate.app/en/experimental-design/sensitivity-analysis-with-event-tree-analysis
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Event Tree AnalysisReliability↔ compare
- Failure Mode and Effects AnalysisExperimental design↔ compare
- Fault Tree AnalysisReliability↔ compare
- Risk-based event tree analysisExperimental design↔ compare
- Sensitivity analysis with fault tree analysisExperimental design↔ compare
- Sensitivity Analysis with Reliability AnalysisExperimental design↔ compare