The predictive power of the first-stage propensity score (PS) model is commonly reported in clinical publications via c-statistics for logistic regressions. A c-statistic greater than 0.80 was recommended in a recent publication. However, we argue that a cut-off like this may not be the best determinant of the first stage PS model, and it is a misconception that the higher predictive power always implies a better PS model. A better way to assess the PS model is to study the relationships between variables of observed confounders, treatment assignment, and outcomes, while the c-statistic can help check the model adequacy. We recommend researchers not blindly craving for high predictive powers with large c-statistics when building the first-stage PS models.