How should missing data be handled in cohort studies?

• A. Use methods like multiple imputation, maximum likelihood, or sensitivity analyses; assess baseline characteristics and potential bias from missingness.
• B. Ignore missing data.
• C. Exclude any individual with missing data.
• D. Use last observation carried forward.

Answer: (A)

Handling missing data properly in cohort studies involves using approaches that use all available information and assess how missingness might bias the results. The best practice is to use methods like multiple imputation, maximum likelihood, and sensitivity analyses, along with checking baseline characteristics to understand potential bias from missingness.

Multiple imputation fills in missing values by modeling the relationships among observed data, creating several complete datasets, analyzing each one, and then combining the results to reflect the uncertainty due to missing data. This helps preserve sample size and reduce bias when data are missing at random or under plausible assumptions.

Maximum likelihood uses the observed data to estimate model parameters directly, leveraging all available information without imputing missing values. This approach is efficient and can produce unbiased estimates under reasonable missing-data assumptions (often MAR).

Sensitivity analyses explore how conclusions change under different assumptions about the missing data mechanism, such as best- or worst-case scenarios or using alternative models. This is crucial because the truth about why data are missing is usually uncertain; showing that results are robust to a range of possibilities strengthens the findings.

Additionally, assess baseline characteristics and potential bias from missingness by comparing those with complete data to those with missing data. If missingness relates to key variables (like exposure or outcome), it can bias estimates, signaling the need for adjustment through modeling choices or weighting.

Why other common approaches are less appropriate: ignoring missing data or excluding individuals with missing data can introduce bias and reduce power when data are not missing completely at random. Last observation carried forward makes strong, often invalid assumptions about stability over time and can distort both results and uncertainty.

In short, use principled methods to handle missing data and verify the robustness of your conclusions with sensitivity analyses and thorough assessment of how missingness relates to key study variables.