A more interpretable regression model for count data with excess of zeros

Abstract

Count data are common in medical research. When these data have more zeros than expected by the most used count distributions, it is common to employ a zero-inflated regression model. However, the interpretability of these models is much lower than the most used count regression models. We present a more interpretable regression model that estimates the mean event rate and models covariate-dependent dispersion directly. Additionally, the dispersion parameter can be interpreted as an index of clumping, a control parameter for overdispersion. We discuss inferential and diagnostic tools and perform a Monte Carlo simulation study to evaluate the performance of the maximum likelihood estimator. Finally, the usefulness of the proposed regression model is illustrated through an application on antenatal care visits.