Example 51.7 ROC Curve, Customized Odds Ratios, Goodness-of-Fit Statistics, R-Square, and Confidence Limits

This example plots an ROC curve, estimates a customized odds ratio, produces the traditional goodness-of-fit analysis, displays the generalized measures for the fitted model, calculates the normal confidence intervals for the regression parameters, and produces a display of the probability function and prediction curves for the fitted model. The data consist of three variables: n (number of subjects in the sample), disease (number of diseased subjects in the sample), and age (age for the sample). A linear logistic regression model is used to study the effect of age on the probability of contracting the disease. The statements to produce the data set and perform the analysis are as follows:

   data Data1; 
      input disease n age;
      datalines;
    0 14 25
    0 20 35
    0 19 45
    7 18 55
    6 12 65
   17 17 75
   ;

   ods graphics on;
   proc logistic data=Data1 plots(only)=(roc(id=obs) effect);
      model disease/n=age / scale=none
                            clparm=wald
                            clodds=pl
                            rsquare;
      units age=10;
   run;
   ods graphics off;

The option SCALE=NONE is specified to produce the deviance and Pearson goodness-of-fit analysis without adjusting for overdispersion. The RSQUARE option is specified to produce generalized measures of the fitted model. The CLPARM=WALD option is specified to produce the Wald confidence intervals for the regression parameters. The UNITS statement is specified to produce customized odds ratio estimates for a change of 10 years in the age variable, and the CLODDS=PL option is specified to produce profile-likelihood confidence limits for the odds ratio. The ODS Graphics statement and the PLOTS= option produce graphical displays of the ROC curve of the model fit.

The results in Output 51.7.1 show that the deviance and Pearson statistics indicate no lack of fit in the model.