Introduction
NB
REGRESSION
Zero-inflated (ZI) non-negative count data frequently arise in medical studies, e.g., number of clinic visits, admissions, days in hospital, number of serious illnesses, and medical costs. This data has the following distinct characteristics: (1) the presence of a large proportion of zero values (i.e., zero inflation [While ZI models have been previously compared to their non-inflated counterparts, the conclusions of which model outperforms the other have been inconsistent. For example, Du et al. [The current study is motivated by a recent clinical trial where, under a Bayesian framework, the ZINB model did not sufficiently outperform the NB model when modeling ZI count outcomes obtained in a trial of children with medical complexity [In this simulation study, we re-analyzed the trial data under a Frequentist framework and further investigated which data properties have the largest effect on model fitness under varying sample sizes and degrees of zero inflation. We first compare the model performance of NB and ZINB models based on AIC and then examine whether any characteristics of a ZI outcome influence the model performance and effect sizes. Our hypotheses are as follows. First, we hypothesized that there would be no significant differences between the NB and ZINB regression models in terms of marginal treatment effects, bias, and coverage. Second, we hypothesized that other data characteristics such as skewness and variance of the non-zero part of the data, rather than the number of zero counts in the ZI data and the degree of overdispersion, would be more important in deciding between NB and ZINB models. Last, we hypothesized that the choice of the 