The FREQ Procedure

Example 2.10 Cochran’s Q Test

When a binary response is measured several times or under different conditions, Cochran’s Q tests that the marginal probability of a positive response is unchanged across the times or conditions. When there are more than two response categories, you can use the CATMOD procedure to fit a repeated-measures model.

The data set Drugs contains data for a study of three drugs to treat a chronic disease (Agresti 2002). Forty-six subjects receive drugs A, B, and C. The response to each drug is either favorable ('F') or unfavorable ('U').

proc format;
   value $ResponseFmt 'F'='Favorable'
                      'U'='Unfavorable';
run;
data drugs;
   input Drug_A $ Drug_B $ Drug_C $ Count @@;
   datalines;
F F F  6   U F F  2
F F U 16   U F U  4
F U F  2   U U F  6
F U U  4   U U U  6
;

The following statements create one-way frequency tables of the responses to each drug. The AGREE option produces Cochran’s Q and other measures of agreement for the three-way table. These statements produce Output 2.10.1 through Output 2.10.5.

proc freq data=Drugs;
   tables Drug_A Drug_B Drug_C / nocum;
   tables Drug_A*Drug_B*Drug_C / agree noprint;
   format Drug_A Drug_B Drug_C $ResponseFmt.;
   weight Count;
   title 'Study of Three Drug Treatments for a Chronic Disease';
run;

The one-way frequency tables in Output 2.10.1 provide the marginal response for each drug. For drugs A and B, 61% of the subjects reported a favorable response; for drug C, 35% of the subjects reported a favorable response. Output 2.10.2 and Output 2.10.3 display measures of agreement for the 'Favorable' and 'Unfavorable' levels of drug A, respectively. McNemar’s test shows a strong discordance between drugs B and C when the response to drug A is favorable.

Output 2.10.1: One-Way Frequency Tables

Study of Three Drug Treatments for a Chronic Disease

The FREQ Procedure

Drug_AFrequency Percent
Favorable2860.87
Unfavorable1839.13

Drug_BFrequency Percent
Favorable2860.87
Unfavorable1839.13

Drug_CFrequency Percent
Favorable1634.78
Unfavorable3065.22


Output 2.10.2: Measures of Agreement for Drug A Favorable

McNemar's Test
Chi-SquareDFPr > ChiSq
10.888910.0010

Simple Kappa Coefficient
EstimateStandard
Error
95% Confidence Limits
-0.03280.1167-0.26150.1960


Output 2.10.3: Measures of Agreement for Drug A Unfavorable

McNemar's Test
Chi-SquareDFPr > ChiSq
0.400010.5271

Simple Kappa Coefficient
EstimateStandard
Error
95% Confidence Limits
-0.15380.2230-0.59090.2832


Output 2.10.4 displays the overall kappa coefficient. The small negative value of kappa indicates no agreement between drug B response and drug C response.

Output 2.10.4: Overall Measures of Agreement

Overall Kappa Coefficient
EstimateStandard
Error
95% Confidence Limits
-0.05880.1034-0.26150.1439

Test for Equal Kappas
Chi-SquareDFPr > ChiSq
0.231410.6305


Cochran’s Q is statistically significant (p=0.0145 in Output 2.10.5), which leads to rejection of the hypothesis that the probability of favorable response is the same for the three drugs.

Output 2.10.5: Cochran’s Q Test

Cochran's Q, for Drug_A by
Drug_B by Drug_C
Chi-SquareDFPr > ChiSq
8.470620.0145


Last updated: November 06, 2022