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Exact Analysis Using the STRATA Statement

When you believe there are not enough data or that the data are too sparse, you can perform a stratified exact conditional logistic regression. The following statements perform stratified exact conditional logistic regressions on the original data set by specifying both the STRATA and EXACT statements:

   proc logistic data=Data1 exactonly;
      strata ID;
      model outcome(event='1')=Gall;
      exact Gall / estimate=both;
   run;

Conditional Analysis Using Transformed Data

When each matched set consists of one event and one nonevent, the conditional likelihood is given by

where and are vectors representing the prognostic factors for the event and nonevent, respectively, of the th matched set. This likelihood is identical to the likelihood of fitting a logistic regression model to a set of data with constant response, where the model contains no intercept term and has explanatory variables given by (Breslow; 1982).

To apply this method, the following DATA step transforms each matched pair into a single observation, where the variables Gall and Hyper contain the differences between the corresponding values for the case and the control (case–control). The variable Outcome, which will be used as the response variable in the logistic regression model, is given a constant value of 0 (which is the Outcome value for the control, although any constant, numeric or character, will suffice).

   data Data2;
      set Data1;
      drop id1 gall1 hyper1;
      retain id1 gall1 hyper1 0;
      if (ID = id1) then do;
         Gall=gall1-Gall; Hyper=hyper1-Hyper;
         output;
      end;
      else do;
         id1=ID; gall1=Gall; hyper1=Hyper;
      end;
   run;

Note that there are 63 observations in the data set, one for each matched pair. Since the number of observations is halved, statistics that depend on such as (the section Generalized Coefficient of Determination) will be incorrect. The variable Outcome has a constant value of 0.

In the following statements, PROC LOGISTIC is invoked with the NOINT option to obtain the conditional logistic model estimates. Because the option CLODDS=PL is specified, PROC LOGISTIC computes a 95% profile-likelihood confidence interval for the odds ratio for each predictor variable; note that profile-likelihood confidence intervals are not currently available when a STRATA statement is specified.

   proc logistic data=Data2;
      model outcome=Gall / noint clodds=PL;
   run;

The results are not displayed here.

Exact Analysis Using Transformed Data

Sometimes the original data set in a matched-pairs study is too large for the exact methods to handle. In such cases it might be possible to use the transformed data set. The following statements perform exact conditional logistic regressions on the transformed data set. The results are not displayed here.

   proc logistic data=Data2 exactonly;
      model outcome=Gall / noint;
      exact Gall / estimate=both;
   run;

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