MONOTONE
<method <(<imputed <= effects>> </ options>)>>
<…method <(<imputed <= effects>> </ options>)>> ;
The MONOTONE statement specifies imputation methods for data sets with monotone missingness. You must also specify a VAR statement, and the data set must have a monotone missing pattern with variables ordered in the VAR list.
Table 57.4 summarizes the options available for the MONOTONE statement.
Table 57.4: Summary of Imputation Methods in MONOTONE Statement
Option 
Description 

Specifies the discriminant function method 

Specifies the logistic regression method 

Specifies the propensity scores method 

Specifies the regression method 

Specifies the predictive mean matching method 
For each method, you can specify the imputed variables and, optionally, a set of the effects to impute these variables. Each effect is a variable or a combination of variables preceding the imputed variable in the VAR statement. The syntax for specification of effects is the same as for the GLM procedure. See Chapter 42: The GLM Procedure, for more information.
One general form of an effect involving several variables is
X1 * X2 * A * B * C ( D E )
where A
, B
, C
, D
, and E
are classification variables and X1
and X2
are continuous variables.
If no covariates are specified, then all preceding variables are used as the covariates. That is, each preceding continuous variable is used as a regressor effect, and each preceding classification variable is used as a main effect. For the discriminant function method, only the continuous variables can be used as covariate effects.
When a method for continuous variables is specified without imputed variables, the method is used for all continuous variables in the VAR statement that are not specified in other methods. Similarly, when a method for classification variables is specified without imputed variables, the method is used for all classification variables in the VAR statement that are not specified in other methods.
When a MONOTONE statement is used without specifying any methods, the regression method is used for all continuous variables and the discriminant function method is used for all classification variables. The preceding variables of each imputed variable in the VAR statement are used as the covariates.
With a MONOTONE statement, the variables are imputed sequentially in the order given by the VAR statement. For a continuous variable, you can use a regression method, a regression predicted mean matching method, or a propensity score method to impute missing values.
For a nominal classification variable, you can use a discriminant function method to impute missing values without using the ordering of the class levels. For an ordinal classification variable, you can use a logistic regression method to impute missing values by using the ordering of the class levels. For a binary classification variable, either a discriminant function method or a logistic regression method can be used.
Note that except for the regression method, all other methods impute values from the observed observation values. You can specify the following methods in a MONOTONE statement.
With a MONOTONE statement, the missing values of a variable are imputed when the variable is either explicitly specified in the method or implicitly specified when a method is specified without imputed variables. These variables are imputed sequentially in the order specified in the VAR statement. For example, the following MI procedure statements use the logistic regression method to impute variable from effects , , and first, and then use the regression method to impute variable from effects , , and :
proc mi; class c1; var y1 y2 c1 y3; monotone reg(y3= y1 y2 c1) logistic(c1= y1 y2 y1*y2); run;
The variables and are not imputed since is the leading variable in the VAR statement and is not specified as an imputed variable in the MONOTONE statement.