How do you handle missing data in SAS programming?

Missing data is a common issue in data analysis, and SAS programmers have different ways to handle it. One approach is to use PROC MI to impute missing values based on other variables in the dataset. Another method is to exclude observations with missing data using the DROP= or WHERE statements.

SAS programmers can also use the MEANS or SUMMARY procedures to calculate summary statistics without including missing values. Additionally, they may choose to create a separate variable to indicate missing data or replace missing values with zeros or other placeholders.

Overall, handling missing data in SAS programming requires a good understanding of the dataset and the analysis objectives to choose the appropriate method for imputation or exclusion.