The proportional odds model is a useful tool for analyzing outcomes like migraine severity or treatment effectiveness, which are often measured on an ordinal scale. It helps avoid losing important information by treating the ordinal data as a whole instead of simplifying it into just two categories. The model can also handle situations where data from the same group are related, like in multicenter trials or longitudinal studies. Researchers have developed methods to fit these models efficiently, making it easier to analyze complex data and draw meaningful conclusions.