Comparing zeroinflated Poisson, Poisson gamma, and Poisson lognormal regression models in dental health data

Background Aim: Statistical modeling is one of the most suitable methods for analyzing the relationship between health and medical issues. In the situation of analysis of zero-inflated data, there are different methods. In this study, the models Poisson, Poisson gamma, and Poisson lognormal regression were compared. Methods Materials: This cross-sectional study was conducted to determine the influential factors on decay-missing-filled (DMF) index by the three mentioned models using the data of 808 first-grade children of the primary school in Kerman, Iran. The command PROC NLMIXED in SAS software was applied for fitting the models on data. For comparing the models, we applied the Akaike’s criterion (AIC), mean square error (MSE) criterion and confidence interval (CI). Results: The AIC and CI showed that the Poisson lognormal model was better than the others due to a level of significance. The variables of the students’ place of living, mothers’ jobs, fathers’ jobs, the region, sex, optic problems, and behavioral problems had a significant effect on DMF index. Conclusion: Poisson lognormal was better than the other models in dental health data.