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 63.4 summarizes the options available for the MONOTONE statement.
Table 63.4: 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 45: 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.
When a MONOTONE statement is used without specifying any methods, the regression method is used for all imputed continuous variables and the discriminant function method is used for all imputed classification variables. In this case, for each imputed continuous variable, all preceding variables in the VAR statement are used as the covariates, and for each imputed classification variable, all preceding continuous variables in the VAR statement are used as the covariates.
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.
For each imputed variable that does not use the discriminant function method, if no covariates are specified, then all preceding variables in the VAR statement 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 an imputed variable that uses the discriminant function method, if no covariates are specified, then all preceding variables in the VAR statement are used as the covariates with the CLASSEFFECTS=INCLUDE option, and all preceding continuous variables in the VAR statement are used as the covariates with the CLASSEFFECTS=EXCLUDE option (which is the default).
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 either a discriminant function method or a logistic regression method (generalized logit model) to impute missing values without using the ordering of the class levels. For an ordinal classification variable, you can use a logistic regression method (cumulative logit model) 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 variables with missing values are imputed sequentially in the order specified in the VAR statement. For example, the following MI procedure statements use the default regression method for continuous variables to impute variable from the effect , the logistic regression method to impute variable from effects , , and , and the regression method to impute variable from effects , , and :
proc mi; class c1; var y1 y2 c1 y3; monotone logistic(c1= y1 y2 y1*y2); monotone reg(y3= y1 y2 c1); run;
The variable is not imputed since it is the leading variable in the VAR statement.