Any time researchers calculate results in an observational study, they have to make certain assumptions about what does and does not contribute to those results. Even if (or especially if) they are controlling for or adjusting for certain factors that could confound the results, they will want to test whether the assumptions they made to calculate the data are strong enough to hold up when changing (removing or adding) one or more of those factors or some other input into the calculation. This process is conducting a sensitivity analysis — how sensitive are the results to changes in the mathematical model or other inputs?
Deeper dive
In plainer terms, it’s playing the “what if” game. What happens to the results if they no longer control one confounder? What happens if they add in a different confounder? What if they only analyze the individuals for whom they had 100% complete data? The goal is to find out if the results hold up with each of these different calculations or to predict or discover potential alternative results/outcomes. Sometimes a sensitivity analysis might reveal that a subgroup has a greater or lesser risk than another, or that age doesn’t actually play a role as it was thought to.
Here are some questions a sensitivity analysis might help answer:
- How robust are these findings?
- What are the biggest factors driving these findings?
- If we tweak X, what happens to Y?
- What are the weakest areas of the analysis or model? (Where is there uncertainty?)
- What inputs or factors can we remove without significantly changing the results? (Useful for fine tuning a list of risk factors, for example)
- What future areas of research might be worth pursuing based on these findings or the influence of certain factors?
Sensitivity analyses typically involve complex biostatistics, so most reporters would need to ask a biostatistician to look over one to ensure it looks solid. But not every study would require an in-depth understanding of the sensitivity analysis. It’s helpful to read in a story because it provides insight into how the researchers were thinking, what assumptions they were making, and what they didn’t account for, but a biostatistician’s help would generally only be necessary if something seemed off, didn’t seem to add up, or lacked certain considerations that you think should have been considered.