Discussion
NB
REGRESSION
The aim of this study was to compare the performance of NB and ZINB regression models in terms of bias, MSE, and coverage and to determine which properties of zero-inflated count data are better described by a ZINB model. Our simulation results indicated that a ZINB regression model does not necessarily outperform an NB model when evaluating ZI medical count outcomes obtained in a trial of children with medical complexity. This is consistent with our original analysis conducted under a Bayesian framework. Even when data were simulated from an underlying ZINB distribution, the NB model had a very similar or even smaller relative bias and MSE for the marginal treatment effect. This suggests that when data is explained and predicted using regression coefficients, as is common in medical and epidemiological studies, there is no significant difference between the NB and ZINB models. Additionally, we want to emphasize that determining the best-fitting model using quantitative model selection criteria (e.g., AIC) is not the only goal of statistical modeling. The ultimate goal of statistical modeling, as Hand [When comparing which model gave a better fit to simulated data, our results showed that the NB model outperformed that of a ZINB model in terms of bias, MSE, and coverage for the treatment group coefficients, even with outcomes generated from a ZI distribution. It signifies that, in terms of the results (e.g., an intervention effect) in which medical professionals are primarily interested, there is no substantial difference between the ZI and non-ZI regression models, even when the outcome contains excess zeros. Note that, when we used a primary outcome with a sample size of 800, the coverage from the From the multivariable ridge logistic regression, we observed that the proportion of zeroes played a smaller role in predicting a preference for the ZINB model than the MLE of the shape parameter, when the DD ZINB model was used to generate synthetic primary outcomes. This result indicates that, contrary to popular belief, the percentage of zero counts in predicting a preference for (or fitness of) the ZINB model (over the NB model) is not as substantial as we would assume.For However, there are the following three caveats to consider. First, we exclusively considered the use of the ZINB model (and the ZIP model without any subsequent results described) in this study, which assumes that zero counts are from either the In spite of the caveats discussed, this study is significant because it sheds fresh light on modeling with zero-inflated outcomes, which are frequently observed in medical data. We recommend that the percentage of zero counts in the outcome not be used as the sole and primary reason for selecting ZI regression models. Investigators should also consider other data characteristics such as the mean and skewness of the non-zero part of the outcome when choosing a model for medical count data. In addition, if the performance of the NB and ZINB regression models is reasonably comparable even with ZI outcomes, we advocate the use of the NB regression model due to its clear and straightforward interpretation of the results.