I am using the logistic regression framework to formulate a classification model. I have a dataset with 42 'true' (response variable) values and 4400 'false' ones. By using the ‘rule-of-10’ and other considerations, I have selected four independent variables. My aim is solely to understand the relative importance of each of these variables (if at all) towards determining the level of the dependent variable. In this case, I understand that even with an unbalanced dataset (42 versus 4400), logistic regression could still produce good coefficient estimates. Specifically, wikipedia says: ‘Logistic regression is unique in that it may be estimated on unbalanced data, rather than randomly sampled data, and still yield correct coefficient estimates of the effects of each independent variable on the outcome.’ This also seems to be intuitively correct if one thinks about how the sigmoid function curve is fitted.
Can you please help me with a reference (preferably, a textbook) for this statement? I checked some versions of the textbook by Hosmer and Lemeshow, but could not find anything.