Which item is included as a sensitivity analysis domain?

• A. Covariates
• B. Sample size
• C. Random variance
• D. Outcome measurement error

Answer: (A)

Sensitivity analysis focuses on testing whether study results hold up when you tweak key modeling choices. Varying the covariates included in the model is a common and essential way to assess robustness, because the set of covariates used to adjust for confounding can directly influence the observed association. By re-running analyses with different covariate sets or with covariates entered in alternative forms, you check whether the main findings persist. If the estimated effect remains similar across plausible covariate specifications, you gain confidence that the result isn’t driven by a particular choice of adjustment.

Other options don’t align as directly with this typical domain. Sample size isn’t a modeling assumption you usually alter in a sensitivity analysis; it’s a property of the data that affects precision rather than the robustness of an estimate to modeling choices. Random variance reflects inherent randomness rather than a specific analysis domain to modify. Outcome measurement error is indeed a potential source of bias to explore, but the most straightforward and common sensitivity domain among the given choices is covariates, since adjusting for different covariate sets directly tests the stability of the effect under different confounding scenarios.