This script illustrates how to perform multiple imputation when missing data are assumed to be Missing Not At Random (MNAR). It begins by creating a synthetic dataset ('Fcs1') with simulated missing values. Then, it uses `PROC MI` with the `FCS` (Fully Conditional Specification) option and the `MNAR` statement to adjust the imputed values of variables `y1` and `y2` specifically for the treatment group `Trt='1'`, by applying shifts.
Data Analysis
Type : CREATION_INTERNE
The data are artificially generated in the DATA step `Fcs1` using loops and random number generation functions (`rannor`, `ranuni`) to simulate clinical trial data.
1 Code Block
DATA STEP Data
Explanation : Creation of the `Fcs1` dataset. Generates `y0`, `y1`, `y2` variables based on a normal distribution and randomly introduces missing values (`.`) for `y1` or `y2`.
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data Fcs1;
do Trt=0 to 1;
do j=1 to 5;
y0=10 + rannor(99);
y1= y0 + 0.9*Trt + rannor(99);
y2= y0 + 0.9*Trt + rannor(99);
if (ranuni(99) < 0.3) then y1=.;
else if (ranuni(99) < 0.3) then y2=.;
output;
end; end;
do Trt=0 to 1;
do j=1 to 45;
y0=10 + rannor(99);
y1= y0 + 0.9*Trt + rannor(99);
y2= y0 + 0.9*Trt + rannor(99);
if (ranuni(99) < 0.3) then y1=.;
else if (ranuni(99) < 0.3) then y2=.;
output;
end; end;
drop j;
run;
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DATA Fcs1;
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DO Trt=0 to 1;
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DO j=1 to 5;
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y0=10 + rannor(99);
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y1= y0 + 0.9*Trt + rannor(99);
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y2= y0 + 0.9*Trt + rannor(99);
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IF (ranuni(99) < 0.3) THEN y1=.;
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ELSEIF (ranuni(99) < 0.3) THEN y2=.;
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OUTPUT;
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END; END;
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DO Trt=0 to 1;
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DO j=1 to 45;
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y0=10 + rannor(99);
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y1= y0 + 0.9*Trt + rannor(99);
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y2= y0 + 0.9*Trt + rannor(99);
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IF (ranuni(99) < 0.3) THEN y1=.;
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ELSEIF (ranuni(99) < 0.3) THEN y2=.;
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OUTPUT;
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END; END;
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drop j;
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RUN;
2 Code Block
PROC PRINT
Explanation : Displays the first 10 observations of the generated dataset for verification.
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proc print data=Fcs1(obs=10);
var Trt Y0 Y1 Y2;
title 'First 10 Obs in the Trial Data';
run;
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PROC PRINTDATA=Fcs1(obs=10);
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var Trt Y0 Y1 Y2;
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title 'First 10 Obs in the Trial Data';
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RUN;
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PROC MI Data
Explanation : Performs multiple imputation. Uses the `FCS` method with 25 iterations. The `MNAR` statement applies an adjustment (shift of -0.4 for `y1` and -0.5 for `y2`) only for observations where `Trt='1'`, simulating a bias for missing data.
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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;
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PROC MIDATA=Fcs1 seed=52387 out=outex16;
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class Trt;
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fcs nbiter=25 reg( /details);
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mnar adjust( y1 /shift=-0.4 adjustobs=(Trt='1'))
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adjust( y2 /shift=-0.5 adjustobs=(Trt='1'));
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var Trt y0 y1 y2;
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RUN;
4 Code Block
PROC PRINT
Explanation : Displays the first 10 observations of the output dataset `outex16`, which contains the imputed data.
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proc print data=outex16(obs=10);
var _Imputation_ Trt y0 y1 y2;
title 'First 10 Observations of the Imputed Data Set';
run;
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PROC PRINTDATA=outex16(obs=10);
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var _Imputation_ Trt y0 y1 y2;
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title 'First 10 Observations of the Imputed Data Set';
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RUN;
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