The MIANALYZE Procedure

Example 57.10 Combining Correlation Coefficients

This example combines sample correlation coefficients computed from a set of imputed data sets by using Fisher’s transformation.

Fisher’s transformation of the sample correlation is

The statistic is approximately normally distributed with mean

and variance , where is the population correlation coefficient and is the number of observations.

The following statements use the CORR procedure to compute the correlation and its associated Fisher’s statistic between variables Oxygen and RunTime for each imputed data set. The ODS statement is used to save Fisher’s statistic in an output data set.

proc corr data=outmi fisher(biasadj=no);
   var Oxygen RunTime;
   by _Imputation_;
   ods output FisherPearsonCorr= outz;
run;

The following statements display the number of observations and Fisher’s statistic for each imputed data set in Output 57.10.1:

proc print data=outz;
   title 'Fisher''s Correlation Statistics';
   var _Imputation_ NObs ZVal;
run;

Output 57.10.1 Output Statistics

Fisher's Correlation Statistics

Obs	_Imputation_	NObs	ZVal
1	1	31	-1.27869
2	2	31	-1.30715
3	3	31	-1.27922
4	4	31	-1.39243
5	5	31	-1.40146

The following statements generate the standard error associated with the statistic, :

data outz;
   set outz;
   StdZ= 1. / sqrt(NObs-3);
run;

The following statements use the MIANALYZE procedure to generate a combined parameter estimate and its variance, as shown in Output 57.10.2. The ODS statement is used to save the parameter estimates in an output data set.

proc mianalyze data=outz;
   ods output ParameterEstimates=parms;
   modeleffects ZVal;
   stderr StdZ;
run;

Output 57.10.2 Combining Fisher’s Statistics

The MIANALYZE Procedure

Parameter Estimates
Parameter	Estimate	Std Error	95% Confidence Limits		DF	Minimum	Maximum	Theta0	t for H0: Parameter=Theta0	Pr > |t|
ZVal	-1.331787	0.200327	-1.72587	-0.93771	330.23	-1.401459	-1.278686	0	-6.65	<.0001

In addition to the estimate for , PROC MIANALYZE also generates confidence limits for , and . The following statements print the estimate and confidence limits for in Output 57.10.3:

proc print data=parms;
   title 'Parameter Estimates with 95% Confidence Limits';
   var Estimate LCLMean UCLMean;
run;

Output 57.10.3 Parameter Estimates with Confidence Limits

Parameter Estimates with 95% Confidence Limits

Obs	Estimate	LCLMean	UCLMean
1	-1.331787	-1.72587	-0.93771

An estimate of the correlation coefficient with its corresponding confidence limits is then generated from the following inverse transformation as described in the section Correlation Coefficients:

for , , and .

The following statements generate and display an estimate of the correlation coefficient and its confidence limits, as shown in Output 57.10.4:

data corr_ci;
   set parms;
   r=       tanh( Estimate);
   r_lower= tanh( LCLMean);
   r_upper= tanh( UCLMean);
run;
proc print data=corr_ci;
   title 'Estimated Correlation Coefficient'
         ' with 95% Confidence Limits';
   var r r_lower r_upper;
run;