Clary relies on ‘rare events regression’, which biases against wars
Dr. Paul Allison 12, Ph.D., Professor of Sociology at the University of Pennsylvania Where He Teaches Graduate Courses In Methods And Statistics, “Logistic Regression for Rare Events”, 2/13/2012, http://statisticalhorizons.com/logistic-regression-for-rare-events
Prompted by a 2001 article by King and Zeng, many researchers worry about whether they can legitimately use conventional logistic regression for data in which events are rare. Although King and Zeng accurately described the problem and proposed an appropriate solution, there are still a lot of misconceptions about this issue.
The problem is not specifically the rarity of events, but rather the possibility of a small number of cases on the rarer of the two outcomes. If you have a sample size of 1000 but only 20 events, you have a problem. If you have a sample size of 10,000 with 200 events, you may be OK. If your sample has 100,000 cases with 2000 events, you’re golden.
There’s nothing wrong with the logistic model in such cases. The problem is that maximum likelihood estimation of the logistic model is well-known to suffer from small-sample bias. And the degree of bias is strongly dependent on the number of cases in the less frequent of the two categories. So even with a sample size of 100,000, if there are only 20 events in the sample, you may have substantial bias.