Polytomous outcomes are common in epidemiologic studies. Analyses based on multinomial models employ a likelihood that utilizes the data observed in all outcome categories simultaneously and permits inferences regarding associations across outcome categories. However, the potentially large number of estimated parameters produced by multinomial model fitting can lead to problems of estimation and inference (1). We have proposed an inverse-probability-of-exposure weighted multinomial model for analysis of polytomous outcomes, described its implementation, and illustrated it. The approach yields marginal estimates of associations, which are sometimes desirable as summary measures of association (2). This approach allows for confounding control and tends to be less susceptible to problems of estimation that arise when at least one outcome category is rare
