Let us look at some of the frequently used statistical procedures in SAS and their handling of Missing values.
For each variable, the number of non-missing values are used
By default, missing values are excluded and percentages are based on the number of non-missing values. If you use the missing option on the tables statement, the percentages are based on the total number of observations (non-missing and missing) and the percentage of missing values are reported in the table.
By default, correlations are computed based on the number of pairs with non-missing data (pairwise deletion of missing data). The nomiss option can be used on the proc corr statement to request that correlations be computed only for observations that have non-missing data for all variables on the var statement (listwise deletion of missing data).
If any of the variables on the model or var statement are missing, they are excluded from the analysis (i.e., listwise deletion of missing data)
Missing values are deleted listwise, i.e., observations with missing values on any of the variables in the analysis are omitted from the analysis.
The handling of missing values in proc glm can be complex to explain. If you have an analysis with just one variable on the left side of the model statement (just one outcome or dependent variable), observations are eliminated if any of the variables on the model statement are missing.
7.Likewise, if you are performing a repeated measures ANOVA or a MANOVA, then observations are eliminated if any of the variables in the model statement are missing. For other situations, see the SAS/STAT manual about proc glm.