**Sensitivity analysis**is post-hoc analysis which tells us how robust our results are. It can give specific information on:

- Which assumptions are important, and how much they affect research results,
- How changes in methods, models, or the values of unmeasured variables affect results.

Sensitivity analysis is also known as “what-if” analysis; It focuses on what happens to the dependent variable when various parameters change. It is important in all fields of scientific and statistical research.

## Purposes of Sensitivity Analysis

Sensitivity Analysis can help you to find important connections between:

- Model inputs,
- Predictions and forecasts,
- Observations.

It gives you a way to locate sensitive parameters (i.e. those that have a great effect on the model). In addition, it can help you to identify non-sensitive parameters. This means that it may help you simplify models, by eliminating:

- Input variables which have no real affect on the data,
- Redundant structures.

Unexpected relationships between parameters and results can point to errors in your model. Sensitivity analysis can also help you redesign your experiments; as you find which parameters are most sensitive, you can redesign the experiment to decrease uncertainty in that parameter.

## One-at-a-time (OAT or OFAT) Analysis

One-at-a-time analysis (also known a one-factor-at-a-time, or OFAT, analysis)is one of the simplest ways to analyze a model.

- All but one variable are kept at a baseline;
- That single variable is varied while new readings are taken.
- Next, the test variable is returned to its baseline value, and another reading is taken.
- Each parameter is tested in this way one at a time.

The weak point of this method is that, in focusing on each variable individually, it doesn’t make allowances for interaction between variables. It also doesn’t pinpoint joint effects; effects which might happen when several variables change simultaneously.

## References

Daniels, M. & Hogan, J. Missing Data in Longitudinal Studies.

Pannel, David. Sensitivity analysis: strategies, methods, concepts, examples. Modified from Pannell, D.J. (1997). Sensitivity analysis of normative economic models: Theoretical framework and practical strategies, Agricultural Economics 16: 139-152. Retrieved from http://dpannell.fnas.uwa.edu.au/dpap971f.htm on July 5, 2018

Financial Modeling Techniques: Sensitivity Analysis (“What if” Analysis). Retrieved from https://www.wallstreetprep.com/knowledge/financial-modeling-techniques-sensitivity-what-if-analysis-2/ on July 5, 2018.

Thabane et al, A tutorial on sensitivity analyses in clinical trials: the what, why, when and how. BMC Medical Research Methodology201313:92

https://doi.org/10.1186/1471-2288-13-92. Retrieved from https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-13-92 on July 5th, 2018