This article compared standard regression (logistic), propensity score weighting, propensity score matching, and difference-in-difference (DID) methods in determining the impact of second-generation antidepressant (AD) use on mania-related visits among adult patients with bipolar disorder. Using a large managed care claims database, a logistic regression was developed as a standard approach to predict the likelihood of having mania-related visits after receiving various types of treatments (AD monotherapy, mood stabilizer (MS) monotherapy, and AD-MS combination therapy) controlling for individual baseline characteristics. The propensity score method predicted the propensity to be with one treatment type versus another in the first-stage. Both weighting and greedy matching approaches were applied in the second-stage outcome model. For the DID method, a logistic regression was applied to predict the differential likelihood of having mania-related visits in post-baseline versus baseline periods on different treatments. Both full sample and propensity score-matched sample were applied for the DID method. Except DID with full sample, the results from all other methods suggested no higher likelihood of mania-related visits for second-generation AD-related therapies compared to MS monotherapy. We concluded that standard regression, propensity scoring, and DID methods may produce inconsistent outcomes in a logistic regression framework, when patient baseline characteristics are different between comparison groups and/or not all potential confounders can be correctly measured and fully controlled. Researchers need to be cautious of the basic assumptions and sensitivities of various methods before making a final conclusion. The DID method may be considered in outcome studies when pre-and-post data are available.