Consider a study of the analgesic effects of treatments on elderly patients with neuralgia. Two test treatments and a placebo
are compared. The response variable is whether the patient reported pain or not. Researchers recorded the age and gender of
60 patients and the duration of complaint before the treatment began. The following DATA step creates the data set Neuralgia
:
data Neuralgia; input Treatment $ Sex $ Age Duration Pain $ @@; datalines; P F 68 1 No B M 74 16 No P F 67 30 No P M 66 26 Yes B F 67 28 No B F 77 16 No A F 71 12 No B F 72 50 No B F 76 9 Yes A M 71 17 Yes A F 63 27 No A F 69 18 Yes B F 66 12 No A M 62 42 No P F 64 1 Yes A F 64 17 No P M 74 4 No A F 72 25 No P M 70 1 Yes B M 66 19 No B M 59 29 No A F 64 30 No A M 70 28 No A M 69 1 No B F 78 1 No P M 83 1 Yes B F 69 42 No B M 75 30 Yes P M 77 29 Yes P F 79 20 Yes A M 70 12 No A F 69 12 No B F 65 14 No B M 70 1 No B M 67 23 No A M 76 25 Yes P M 78 12 Yes B M 77 1 Yes B F 69 24 No P M 66 4 Yes P F 65 29 No P M 60 26 Yes A M 78 15 Yes B M 75 21 Yes A F 67 11 No P F 72 27 No P F 70 13 Yes A M 75 6 Yes B F 65 7 No P F 68 27 Yes P M 68 11 Yes P M 67 17 Yes B M 70 22 No A M 65 15 No P F 67 1 Yes A M 67 10 No P F 72 11 Yes A F 74 1 No B M 80 21 Yes A F 69 3 No ;
The data set Neuralgia
contains five variables: Treatment
, Sex
, Age
, Duration
, and Pain
. The last variable, Pain
, is the response variable. A specification of Pain
=Yes indicates there was pain, and Pain
=No indicates no pain. The variable Treatment
is a categorical variable with three levels: A and B represent the two test treatments, and P represents the placebo treatment.
The gender of the patients is given by the categorical variable Sex
. The variable Age
is the age of the patients, in years, when treatment began. The duration of complaint, in months, before the treatment began
is given by the variable Duration
.
The following statements use the LOGISTIC procedure to fit a twoway logit with interaction model for the effect of Treatment
and Sex
, with Age
and Duration
as covariates. The categorical variables Treatment
and Sex
are declared in the CLASS statement.
proc logistic data=Neuralgia; class Treatment Sex; model Pain= Treatment Sex Treatment*Sex Age Duration / expb; run;
In this analysis, PROC LOGISTIC models the probability of no pain (Pain
=No). By default, effect coding is used to represent the CLASS variables. Two design variables are created for Treatment
and one for Sex
, as shown in Output 54.2.1.
Output 54.2.1: Effect Coding of CLASS Variables
Class Level Information  

Class  Value  Design Variables  
Treatment  A  1  0 
B  0  1  
P  1  1  
Sex  F  1  
M  1 
PROC LOGISTIC displays a table of the Type 3 analysis of effects based on the Wald test (Output 54.2.2). Note that the Treatment
*Sex
interaction and the duration of complaint are not statistically significant (p = 0.9318 and p = 0.8752, respectively). This indicates that there is no evidence that the treatments affect pain differently in men and
women, and no evidence that the pain outcome is related to the duration of pain.
Output 54.2.2: Wald Tests of Individual Effects
Type 3 Analysis of Effects  

Effect  DF  Wald ChiSquare 
Pr > ChiSq 
Treatment  2  11.9886  0.0025 
Sex  1  5.3104  0.0212 
Treatment*Sex  2  0.1412  0.9318 
Age  1  7.2744  0.0070 
Duration  1  0.0247  0.8752 
Parameter estimates are displayed in Output 54.2.3. The Exp(Est) column contains the exponentiated parameter estimates requested with the EXPB option. These values can, but do not necessarily, represent odds ratios for the corresponding variables. For continuous explanatory
variables, the Exp(Est) value corresponds to the odds ratio for a unit increase of the corresponding variable. For CLASS variables
that use effect coding, the Exp(Est) values have no direct interpretation as a comparison of levels. However, when the reference
coding is used, the Exp(Est) values represent the odds ratio between the corresponding level and the reference level. Following
the parameter estimates table, PROC LOGISTIC displays the odds ratio estimates for those variables that are not involved in
any interaction terms. If the variable is a CLASS variable, the odds ratio estimate comparing each level with the reference
level is computed regardless of the coding scheme. In this analysis, since the model contains the Treatment
*Sex
interaction term, the odds ratios for Treatment
and Sex
were not computed. The odds ratio estimates for Age
and Duration
are precisely the values given in the Exp(Est) column in the parameter estimates table.
Output 54.2.3: Parameter Estimates with Effect Coding
Analysis of Maximum Likelihood Estimates  

Parameter  DF  Estimate  Standard Error 
Wald ChiSquare 
Pr > ChiSq  Exp(Est)  
Intercept  1  19.2236  7.1315  7.2661  0.0070  2.232E8  
Treatment  A  1  0.8483  0.5502  2.3773  0.1231  2.336  
Treatment  B  1  1.4949  0.6622  5.0956  0.0240  4.459  
Sex  F  1  0.9173  0.3981  5.3104  0.0212  2.503  
Treatment*Sex  A  F  1  0.2010  0.5568  0.1304  0.7180  0.818 
Treatment*Sex  B  F  1  0.0487  0.5563  0.0077  0.9302  1.050 
Age  1  0.2688  0.0996  7.2744  0.0070  0.764  
Duration  1  0.00523  0.0333  0.0247  0.8752  1.005 
Odds Ratio Estimates  

Effect  Point Estimate  95% Wald Confidence Limits 

Age  0.764  0.629  0.929 
Duration  1.005  0.942  1.073 
The following PROC LOGISTIC statements illustrate the use of forward selection on the data set Neuralgia
to identify the effects that differentiate the two Pain
responses. The option SELECTION=FORWARD is specified to carry out the forward selection. The term TreatmentSex@2
illustrates another way to specify main effects and twoway interactions. (Note that, in this case, the “@2” is unnecessary because no interactions besides the twoway interaction are possible).
proc logistic data=Neuralgia; class Treatment Sex; model Pain=TreatmentSex@2 Age Duration /selection=forward expb; run;
Results of the forward selection process are summarized in Output 54.2.4. The variable Treatment
is selected first, followed by Age
and then Sex
. The results are consistent with the previous analysis (Output 54.2.2) in which the Treatment
*Sex
interaction and Duration
are not statistically significant.
Output 54.2.4: Effects Selected into the Model
Summary of Forward Selection  

Step  Effect Entered 
DF  Number In 
Score ChiSquare 
Pr > ChiSq 
1  Treatment  2  1  13.7143  0.0011 
2  Age  1  2  10.6038  0.0011 
3  Sex  1  3  5.9959  0.0143 
Output 54.2.5 shows the Type 3 analysis of effects, the parameter estimates, and the odds ratio estimates for the selected model. All three
variables, Treatment
, Age
, and Sex
, are statistically significant at the 0.05 level (p=0.0018, p=0.0213, and p=0.0057, respectively). Since the selected model does not contain the Treatment
*Sex
interaction, odds ratios for Treatment
and Sex
are computed. The estimated odds ratio is 24.022 for treatment A versus placebo, 41.528 for Treatment B versus placebo, and
6.194 for female patients versus male patients. Note that these odds ratio estimates are not the same as the corresponding
values in the Exp(Est) column in the parameter estimates table because effect coding was used. From Output 54.2.5, it is evident that both Treatment A and Treatment B are better than the placebo in reducing pain; females tend to have better
improvement than males; and younger patients are faring better than older patients.
Output 54.2.5: Type 3 Effects and Parameter Estimates with Effect Coding
Type 3 Analysis of Effects  

Effect  DF  Wald ChiSquare 
Pr > ChiSq 
Treatment  2  12.6928  0.0018 
Sex  1  5.3013  0.0213 
Age  1  7.6314  0.0057 
Analysis of Maximum Likelihood Estimates  

Parameter  DF  Estimate  Standard Error 
Wald ChiSquare 
Pr > ChiSq  Exp(Est)  
Intercept  1  19.0804  6.7882  7.9007  0.0049  1.9343E8  
Treatment  A  1  0.8772  0.5274  2.7662  0.0963  2.404 
Treatment  B  1  1.4246  0.6036  5.5711  0.0183  4.156 
Sex  F  1  0.9118  0.3960  5.3013  0.0213  2.489 
Age  1  0.2650  0.0959  7.6314  0.0057  0.767 
Odds Ratio Estimates  

Effect  Point Estimate  95% Wald Confidence Limits 

Treatment A vs P  24.022  3.295  175.121 
Treatment B vs P  41.528  4.500  383.262 
Sex F vs M  6.194  1.312  29.248 
Age  0.767  0.636  0.926 
Finally, the following statements refit the previously selected model, except that reference coding is used for the CLASS variables instead of effect coding:
ods graphics on; proc logistic data=Neuralgia plots(only)=(oddsratio(range=clip)); class Treatment Sex /param=ref; model Pain= Treatment Sex Age; oddsratio Treatment; oddsratio Sex; oddsratio Age; contrast 'Pairwise A vs P' Treatment 1 0 / estimate=exp; contrast 'Pairwise B vs P' Treatment 0 1 / estimate=exp; contrast 'Pairwise A vs B' Treatment 1 1 / estimate=exp; contrast 'Female vs Male' Sex 1 / estimate=exp; effectplot / at(Sex=all) noobs; effectplot slicefit(sliceby=Sex plotby=Treatment) / noobs; run; ods graphics off;
The ODDSRATIO statements compute the odds ratios for the covariates. Four CONTRAST statements are specified; they provide another method of producing the odds ratios. The three contrasts labeled 'Pairwise'
specify a contrast vector, L, for each of the pairwise comparisons between the three levels of Treatment
. The contrast labeled 'Female vs Male' compares female to male patients. The option ESTIMATE=EXP is specified in all CONTRAST statements to exponentiate the estimates of . With the given specification of contrast coefficients, the first of the 'Pairwise' CONTRAST statements corresponds to the
odds ratio of A versus P, the second corresponds to B versus P, and the third corresponds to A versus B. You can also specify
the 'Pairwise' contrasts in a single contrast statement with three rows. The 'Female vs Male' CONTRAST statement corresponds
to the odds ratio that compares female to male patients.
The PLOTS(ONLY)= option displays only the requested odds ratio plot when ODS Graphics is enabled. The EFFECTPLOT statements do not honor the ONLY option, and display the fitted model. The first EFFECTPLOT statement by default produces a plot of the predicted values against the continuous Age
variable, grouped by the Treatment
levels. The AT option produces one plot for males and another for females; the NOOBS option suppresses the display of the observations. In the second EFFECTPLOT statement, a SLICEFIT plot is specified to display the Age
variable on the X axis, the fits are grouped by the Sex
levels, and the PLOTBY= option produces a panel of plots that displays each level of the Treatment
variable.
The reference coding is shown in Output 54.2.6. The Type 3 analysis of effects, the parameter estimates for the reference coding, and the odds ratio estimates are displayed in Output 54.2.7. Although the parameter estimates are different because of the different parameterizations, the “Type 3 Analysis of Effects” table and the “Odds Ratio” table remain the same as in Output 54.2.5. With effect coding, the treatment A parameter estimate (0.8772) estimates the effect of treatment A compared to the average effect of treatments A, B, and placebo. The treatment A estimate (3.1790) under the reference coding estimates the difference in effect of treatment A and the placebo treatment.
Output 54.2.6: Reference Coding of CLASS Variables
Class Level Information  

Class  Value  Design Variables  
Treatment  A  1  0 
B  0  1  
P  0  0  
Sex  F  1  
M  0 
Output 54.2.7: Type 3 Effects and Parameter Estimates with Reference Coding
Type 3 Analysis of Effects  

Effect  DF  Wald ChiSquare 
Pr > ChiSq 
Treatment  2  12.6928  0.0018 
Sex  1  5.3013  0.0213 
Age  1  7.6314  0.0057 
Analysis of Maximum Likelihood Estimates  

Parameter  DF  Estimate  Standard Error 
Wald ChiSquare 
Pr > ChiSq  
Intercept  1  15.8669  6.4056  6.1357  0.0132  
Treatment  A  1  3.1790  1.0135  9.8375  0.0017 
Treatment  B  1  3.7264  1.1339  10.8006  0.0010 
Sex  F  1  1.8235  0.7920  5.3013  0.0213 
Age  1  0.2650  0.0959  7.6314  0.0057 
The ODDSRATIO statement results are shown in Output 54.2.8, and the resulting plot is displayed in Output 54.2.9. Note in Output 54.2.9 that the odds ratio confidence limits are truncated due to specifying the RANGE=CLIP option; this enables you to see which intervals contain “1” more clearly. The odds ratios are identical to those shown in the “Odds Ratio Estimates” table in Output 54.2.7 with the addition of the odds ratio for “Treatment A vs B”. Both treatments A and B are highly effective over placebo in reducing pain, as can be seen from the odds ratios comparing
treatment A against P and treatment B against P (the second and third rows in the table). However, the 95% confidence interval
for the odds ratio comparing treatment A to B is (0.0932, 3.5889), indicating that the pain reduction effects of these two
test treatments are not very different. Again, the ’Sex F vs M’ odds ratio shows that female patients fared better in obtaining
relief from pain than male patients. The odds ratio for Age
shows that a patient one year older is 0.77 times as likely to show no pain; that is, younger patients have more improvement
than older patients.
Output 54.2.8: Results from the ODDSRATIO Statements
Odds Ratio Estimates and Wald Confidence Intervals  

Label  Estimate  95% Confidence Limits  
Treatment A vs B  0.578  0.093  3.589 
Treatment A vs P  24.022  3.295  175.121 
Treatment B vs P  41.528  4.500  383.262 
Sex F vs M  6.194  1.312  29.248 
Age  0.767  0.636  0.926 
Output 54.2.9: Plot of the ODDSRATIO Statement Results
Output 54.2.10 contains two tables: the “Contrast Test Results” table and the “Contrast Estimation and Testing Results by Row” table. The former contains the overall Wald test for each CONTRAST statement. The latter table contains estimates and tests of individual contrast rows. The estimates for the first two rows of the ’Pairwise’ CONTRAST statements are the same as those given in the two preceding odds ratio tables (Output 54.2.7 and Output 54.2.8). The third row estimates the odds ratio comparing A to B, agreeing with Output 54.2.8, and the last row computes the odds ratio comparing pain relief for females to that for males.
Output 54.2.10: Results of CONTRAST Statements
Contrast Test Results  

Contrast  DF  Wald ChiSquare 
Pr > ChiSq 
Pairwise A vs P  1  9.8375  0.0017 
Pairwise B vs P  1  10.8006  0.0010 
Pairwise A vs B  1  0.3455  0.5567 
Female vs Male  1  5.3013  0.0213 
Contrast Estimation and Testing Results by Row  

Contrast  Type  Row  Estimate  Standard Error 
Alpha  Confidence Limits  Wald ChiSquare 
Pr > ChiSq  
Pairwise A vs P  EXP  1  24.0218  24.3473  0.05  3.2951  175.1  9.8375  0.0017 
Pairwise B vs P  EXP  1  41.5284  47.0877  0.05  4.4998  383.3  10.8006  0.0010 
Pairwise A vs B  EXP  1  0.5784  0.5387  0.05  0.0932  3.5889  0.3455  0.5567 
Female vs Male  EXP  1  6.1937  4.9053  0.05  1.3116  29.2476  5.3013  0.0213 
ANCOVAstyle plots of the modelpredicted probabilities against the Age
variable for each combination of Treatment
and Sex
are displayed in Output 54.2.11 and Output 54.2.12. These plots confirm that females always have a higher probability of pain reduction in each treatment group, the placebo
treatment has a lower probability of success than the other treatments, and younger patients respond to treatment better than
older patients.
Output 54.2.11: ModelPredicted Probabilities by Sex
Output 54.2.12: ModelPredicted Probabilities by Treatment