How should missing data be treated in DEOCS analysis?

• A. Exclude any case with missing data from all analyses.
• B. Ignore missing data and proceed with analysis.
• C. Assess missingness, use appropriate methods, and report the extent of missing data.
• D. Do not address missing data in the analysis.

Answer: (C)

Handling missing data in DEOCS analysis begins with understanding why data are missing and what pattern they show. You assess whether data are missing completely at random, missing at random, or not missing at random. Based on that assessment, you choose appropriate methods that preserve information, such as multiple imputation or full information maximum likelihood, and you document the amount and pattern of missing data by variable. This transparency helps determine whether missing data could bias conclusions and allows readers to judge robustness. Excluding cases with any missing data discards information and often introduces bias; simply ignoring missing data or not addressing it misses the chance to correct for potential bias. So the recommended approach is to assess missingness, apply suitable methods, and clearly report the extent of missing data.