What are common ancillary analyses in cohort studies to test robustness?

• A. Sensitivity analyses with different exposure definitions, lag times, and subgroups; multiple imputation for missing data
• B. Only primary analysis
• C. Increasing sample size
• D. Randomization

Answer: (A)

Testing robustness in a cohort study involves checking whether results hold up under different analytic choices and data assumptions. Sensitivity analyses do exactly this by varying how exposure is defined, trying different lag times to account for delays or reverse causation, and examining results in various subgroups. If the findings remain consistent across these alternate specifications, we gain confidence that the association isn’t driven by a particular definition or subset. Addressing missing data with multiple imputation is another key part, because how you handle missing values can bias results. Imputing missing data under reasonable assumptions and combining results across multiple imputed datasets helps ensure that conclusions aren’t driven by the pattern of missingness. In contrast, doing only the primary analysis, simply increasing sample size, or applying randomization in an observational cohort doesn’t provide the same assessment of robustness why the observed associations hold under different analytic choices and data scenarios.