This example computes chisquare tests and Fisher’s exact test to compare the probability of coronary heart disease for two types of diet. It also estimates the relative risks and computes exact confidence limits for the odds ratio.
The data set FatComp
contains hypothetical data for a casecontrol study of high fat diet and the risk of coronary heart disease. The data are
recorded as cell counts, where the variable Count
contains the frequencies for each exposure and response combination. The data set is sorted in descending order by the variables
Exposure
and Response
, so that the first cell of the table contains the frequency of positive exposure and positive response. The FORMAT procedure creates formats to identify
the type of exposure and response with character values.
proc format; value ExpFmt 1='High Cholesterol Diet' 0='Low Cholesterol Diet'; value RspFmt 1='Yes' 0='No'; run;
data FatComp; input Exposure Response Count; label Response='Heart Disease'; datalines; 0 0 6 0 1 2 1 0 4 1 1 11 ;
proc sort data=FatComp; by descending Exposure descending Response; run;
In the following PROC FREQ statements, ORDER=DATA option orders the contingency table values by their order in the input data
set. The TABLES statement requests a twoway table of Exposure
by Response
. The CHISQ option produces several chisquare tests, while the RELRISK option produces relative risk measures. The EXACT
statement requests the exact Pearson chisquare test and exact confidence limits for the odds ratio.
proc freq data=FatComp order=data; format Exposure ExpFmt. Response RspFmt.; tables Exposure*Response / chisq relrisk; exact pchi or; weight Count; title 'CaseControl Study of High Fat/Cholesterol Diet'; run;
The contingency table in Output 40.5.1 displays the variable values so that the first table cell contains the frequency for the first cell in the data set (the frequency of positive exposure and positive response).
Output 40.5.1: Contingency Table
CaseControl Study of High Fat/Cholesterol Diet 


Output 40.5.2 displays the chisquare statistics. Because the expected counts in some of the table cells are small, PROC FREQ gives a warning that the asymptotic chisquare tests might not be appropriate. In this case, the exact tests are appropriate. The alternative hypothesis for this analysis states that coronary heart disease is more likely to be associated with a high fat diet, so a onesided test is desired. Fisher’s exact rightsided test analyzes whether the probability of heart disease in the high fat group exceeds the probability of heart disease in the low fat group; because this pvalue is small, the alternative hypothesis is supported.
The odds ratio, displayed in Output 40.5.3, provides an estimate of the relative risk when an event is rare. This estimate indicates that the odds of heart disease is 8.25 times higher in the high fat diet group; however, the wide confidence limits indicate that this estimate has low precision.
Output 40.5.2: ChiSquare Statistics
Statistic  DF  Value  Prob 

ChiSquare  1  4.9597  0.0259 
Likelihood Ratio ChiSquare  1  5.0975  0.0240 
Continuity Adj. ChiSquare  1  3.1879  0.0742 
MantelHaenszel ChiSquare  1  4.7441  0.0294 
Phi Coefficient  0.4644  
Contingency Coefficient  0.4212  
Cramer's V  0.4644  
WARNING: 50% of the cells have expected counts less than 5. (Asymptotic) ChiSquare may not be a valid test. 