# Offset in Logistic regression: what are the typical use cases?

Offset is commonly used in Poisson regression to take into account different exposure (different time periods for instance): offset = log of exposure

Question: what's the typical use case of offset in logistic regression?

I assume we can't do exposure (proportional effect) in classification problems since E(y|x) can't go beyond 1 so I'm curious about why someone would need to use offset in a logisic regression.

• Why "use cases" and not "examples"? Apr 8 '17 at 15:14
• @Frank Harrell: He is probably a computer type, google "use case" almost all hits are computer things ... Nov 3 '20 at 21:52

When the model is based on oversampled data, offset is used to correct the bias. An alternative is to use weights argument. Note however that offset produces correct probabilities by changing the intercept, usually a judgment based override. On the other hand, weights produces correct parameter estimates by countering the effect of oversampling, as if the model was based on correctly sampled data.