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.
What’s the solution? King and Zeng proposed an alternative estimation method to reduce the bias. Their method is very similar to another method, known as penalized likelihood, that is more widely available in commercial software. Also called the Firth method, after its inventor, penalized likelihood is a general approach to reducing small-sample bias in maximum likelihood estimation. In the case of logistic regression, penalized likelihood also has the attraction of producing finite, consistent estimates of regression parameters when the maximum likelihood estimates do not even exist because of complete or quasi-complete separation.
Unlike exact logistic regression (another estimation method for small samples but one that can be very computationally intensive), penalized likelihood takes almost no additional computing time compared to conventional maximum likelihood. In fact, a case could be made for always using penalized likelihood rather than conventional maximum likelihood for logistic regression, regardless of the sample size. Does anyone have a counter-argument? If so, I’d like to hear it.
You can learn more about penalized likelihood in my seminar Logistic Regression Using SAS.
Gary King and Langche Zeng. “Logistic Regression in Rare Events Data.” Political Analysis 9 (2001): 137-163.