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. In this work, we introduce a more interpretable regression model for count data with excess of zeros based on a reparameterization of the zero-inflated Poisson distribution. 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 children mortality.

翻译：计数数据在医学研究中十分常见。当这些数据中的零值数量超出最常用计数分布模型的预期时，通常需要采用零膨胀回归模型进行分析。然而，此类模型的可解释性远低于最常用的计数回归模型。本研究基于零膨胀泊松分布的重参数化方法，提出了一种针对零膨胀计数数据的更可解释回归模型。我们讨论了模型的推断与诊断工具，并通过蒙特卡洛模拟研究评估了最大似然估计量的性能。最后，通过儿童死亡率分析的应用实例，验证了所提出回归模型的实际效用。