Here are two simple approaches you can use if your logistic model has only one predictor. In

each case, you replace the logistic regression equation with another equation that is somewhat

equivalent, and then do a sample-size calculation based on that. It’s not an ideal solution, but it

can give you an answer that’s close enough for planning purposes.

If the predictor is a dichotomous category (a yes/no variable), logistic regression gives the same

p value you get from analyzing a fourfold table. Therefore, you can use the sample-size

calculations we describe in Chapter 12.

If the predictor is a continuous numerical quantity (like age), you can pretend that the outcome

variable is the predictor, and age is the outcome. We realize this flips the cause-and-effect

relationship backwards, but if you allow that conceptual flip, then you can ask whether the two

different outcome groups have different mean values for the predictor. You can test that question

with an unpaired Student t test, so you can use the sample-size calculations we describe in Chapter

11.