The MI Procedure

(View the complete code for this example.)

This example illustrates the pattern-mixture model approach to multiple imputation under the MNAR assumption by using specified shift parameters to adjust imputed continuous values.

Suppose that a pharmaceutical company is conducting a clinical trial to test the efficacy of a new drug. The trial consists of two groups of equally allocated patients: a treatment group that receives the new drug and a placebo control group. The variable Trt is an indicator variable, with a value of 1 for patients in the treatment group and a value of 0 for patients in the control group. The variable Y0 is the baseline efficacy score, and the variables Y1 and Y2 are the efficacy scores at two successive follow-up visits.

Suppose the data set Fcs1 contains the data from the trial that have possible missing values in Y1 and Y2. Output 76.16.1 lists the first 10 observations in the data set Fcs1.

Also suppose that for the treatment group, the distribution of missing Y1 responses has an expected value that is 0.4 lower than that of the corresponding distribution of the observed Y1 responses. Similarly, the distribution of missing Y2 responses has an expected value that is 0.5 lower than that of the corresponding distribution of the observed Y1 responses.

The following statements adjust the imputed Y1 and Y2 values by –0.4 and –0.5, respectively, for observations in the treatment group:

proc mi data=Fcs1 seed=52387 out=outex16;
   class Trt;
   fcs nbiter=25 reg( /details);
   mnar adjust( y1 /shift=-0.4 adjustobs=(Trt='1'))
        adjust( y2 /shift=-0.5 adjustobs=(Trt='1'));
   var Trt y0 y1 y2;
run;

The MNAR statement imputes missing values for scenarios under the MNAR assumption. The ADJUST option specifies parameters for adjusting the imputed values for specified subsets of observations. The first ADJUST option specifies the shift parameter for the imputed Y1 values for observations for which TRT=1. The second ADJUST option specifies the shift parameter for the imputed Y2 values for observations for which TRT=1.

Because Trt is listed in the VAR statement, it is used as a covariate for other imputed variables in the imputation process. In addition, because Trt is specified in the ADJUSTOBS= suboption, it is also used to select the subset of observations from which the imputed values for the variable are to be adjusted.

The "Model Information" table  in Output 76.16.2 describes the method that is used in the multiple imputation process.

The "FCS Model Specification"  table in Output 76.16.3 describes methods and imputed variables in the imputation model. The MI procedure uses the regression method to impute all the variables.

The "Missing Data Patterns"  table in Output 76.16.4 lists distinct missing data patterns and their corresponding frequencies and percentages.

The "MNAR Adjustments to Imputed Values"  table in Output 76.16.5 lists the adjustment parameters for the five imputations.

The following statements list the first 10 observations of the data set Outex16 in Output 76.16.6:

proc print data=outex16(obs=10);
   var _Imputation_ Trt y0 y1 y2;
   title 'First 10 Observations of the Imputed Data Set';
run;