Example 9.2: Propensity Score Method

The MI Procedure

Example 9.2: Propensity Score Method

This example uses the propensity score method to impute missing values in a data set with a monotone missing pattern. The following statements invoke the MI procedure and request the propensity score method. The resulting data set is named outpscore.

   proc mi data=FitMono seed=55417 simple out=outpscore;
      monotone method=propensity;   
      var Oxygen RunTime RunPulse; 
   run;

Note that the VAR statement is required and the data set must have a monotone missing pattern with variables as ordered in the VAR statement. The procedure generates the following output:

Output 9.2.1: Model Information

The MI Procedure

Model Information
Data Set	WORK.FITMONO
Method	Propensity
Number of Imputations	5
Number of Groups on Propensity	5
Seed for random number generator	55417

The "Model Information" table describes the method and options used in the multiple imputation process. By default, the observations are sorted into five groups based on the propensity scores, and five imputations are created for the missing data.

Output 9.2.2: Missing Data Patterns

The MI Procedure

Missing Data Patterns
Group	Oxygen	RunTime	RunPulse	Freq	Percent	Group Means
Group	Oxygen	RunTime	RunPulse	Freq	Percent	Oxygen	RunTime	RunPulse
1	X	X	X	23	74.19	46.684174	10.776957	170.739130
2	X	X	.	5	16.13	47.505800	10.280000	.
3	X	.	.	3	9.68	52.461667	.	.

The "Missing Data Patterns" table lists distinct missing data patterns with corresponding frequencies and percents. Here, "X" means that the variable is observed in the corresponding group and "." means that the variable is missing. The table also displays group-specific variable means.

Output 9.2.3: Variance Information

The MI Procedure

Multiple Imputation Variance Information
Variable	Variance			DF	Relative Increase in Variance	Fraction Missing Information
Variable	Between	Within	Total	DF	Relative Increase in Variance	Fraction Missing Information
RunTime	0.001068	0.059100	0.060382	27.498	0.021688	0.021448
RunPulse	1.147555	4.686646	6.063711	17.006	0.293828	0.246288

After the completion of m imputations, the "Multiple Imputation Variance Information" table displays the between-imputation variance, within-imputation variance, and total variance for combining complete-data inferences. It also displays the degrees of freedom for the total variance. The relative increase in variance due to missingness and the fraction of missing information for each variable are also displayed. A detailed description of these statistics is provided in the "Combining Inferences from Multiply Imputed Data Sets" section.

The "Multiple Imputation Parameter Estimates" table displays the estimated mean and standard error of the mean for each variable. The inferences are based on the t-distributions. For each variable, the table also displays a 95% mean confidence interval and a t-statistic with the associated p-value for the hypothesis that the population mean is equal to the value specified in the MU0= option, which is zero by default.

Output 9.2.4: Parameter Estimates

The MI Procedure

Multiple Imputation Parameter Estimates
Variable	Mean	Std Error	95% Confidence Limits		DF	Minimum	Maximum	Mu0	t for H0: Mean=Mu0	Pr > \|t\|
RunTime	10.603677	0.245727	10.0999	11.1074	27.498	10.558065	10.648387	0	43.15	<.0001
RunPulse	170.400000	2.462460	165.2048	175.5952	17.006	168.967742	171.838710	0	69.20	<.0001

The following statements list the first ten observations of the data set outpscore.

   proc print data=outpscore(obs=10);
      title 'First 10 Observations of the Imputed Data Set';
   run;

Output 9.2.5: Imputed Data Set

First 10 Observations of the Imputed Data Set

Obs	_Imputation_	Oxygen	RunTime	RunPulse
1	1	44.609	11.37	178
2	1	45.313	10.07	185
3	1	54.297	8.65	156
4	1	59.571	8.63	146
5	1	49.874	9.22	156
6	1	44.811	11.63	176
7	1	45.681	11.95	176
8	1	49.091	10.85	156
9	1	39.442	13.08	174
10	1	60.055	8.63	170

Chapter Contents
Previous
Next
Top