(I work for SAS. My remarks below are just my personal opinions on the subject)
1. One way is exact logistic regression, or other permutation based estimation.
2. 'Regular' logistic regression is known to produce biased estimates sometimes (I read a paper from American Statistics Association long time ago that when the % of 1 is <0.57, bias may occur. Forgot the title of the paper). This stems from the percentage of 1 is too low in the whole model universe. I believe when you say rare events, this is what you mean. In Enterprise Miner 6.2 (and later), when you drag a data set, you can customize in decision prior. And cost and profit and so on. This, however, perhaps is not the answer to your question. For example, your model universe has 0.6% =1 and 99.4%. You call it rare. If you believe the 'true' event (=1) percentage in the population (vs. the sample on your hand due to all kinds of constraints you had in collecting the sample) should be 5% (you know beforehand, that is what the word PRIOR means), you can specify that in Decision and Prior. That, in quantitative sense, makes your model closer to the real population. This reality addresses the bias in term of a healthy, normal percentage structure through reweighting the count of the target. It is, by definition, different from a good model sample that has original event =5%. Through the weighting, you enjoy the cost and benefit of artifically more homogeneous data.
3. In some sense, logistic regression (proc genmod is better than proc logistic in degree, but eventually similar shortcoming on the biasedness) is unfortunate tool for rare event modeling. In Enterprise Miner, look into Rule Induction for a possible better prediction tool. Rare events do not necessarily imply insufficient event counts. In credit card fraud detection models, for example, you can have in your modeling universe over 15,000 fraund cases (event=1), but still rare since the % of <0.5. If your application has sensitive real time action requirement, consider dumping logistic regression entirely. Many applications in medical research or political sciences have rare event ratio where estimation / hypothesis is more critical then prediction, then permutation is the method since those samples are more likely to be fairly small, considering how faster you PC is running today.
Hope this helps.
Jason Xin
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