For this particular study, I had 3 monkeys complete 3 tasks, each task has a binary outcome (Win/Lose). A task is considered complete when they reach a criterion of 85% accuracy in their most recent 120 trials. So for each monkey I have lists, varying in length, of W's and L's. Originally I planed on using a Fisher's exact test, for each individual monkey, to compare the number of Wins and Losses for Test 1 and 2, and then for Test 1 and 3, and for Test 2 and 3. However, I've read that it is not appropriate to use a Fisher's exact test for repeated measures data and was told it may be possible to use a binary logistic regression instead. I've been looking into the binary logistic regression and have found mixed reviews on whether this test is appropriate for repeated measures data. I suppose my question is if binary logistic regression would work for this dataset? and if not, what other model would be more appropriate?
If you want to use a binary logistic regression technique, you'd have to use a longitudinal regression technique which will handle repeated outcomes having values 0,1. Examples are GLM or GEE with a family or "link function" set to "logit," i.e., logistic. GEE has the advantage of not requiring uniform time periods, meaning the time intervals can vary across measurements and animals. There are also "mixed" regression modules which will treat each animal as its own cluster (random effects) in order to accommodate the within-subject correlations.