In epidemiology, many outcomes of interest are naturally ordered. For instance, disease severity can range from mild to severe, or patient satisfaction can range from very dissatisfied to very satisfied. Traditional regression models like linear regression are not suitable for these types of outcomes due to their categorical nature. Therefore, ordinal logistic regression is employed to better capture the ordered nature of such outcomes.
