Background: Outcome measures that are count variables with excessive zeros are common in health behaviors research. There is a lack of empirical data about the relative performance of prevailing statistical models when outcomes are zero-inflated, particularly compared with recently developed approaches. Methods: The current simulation study examined five commonly used analytical approaches for count outcomes, including two linear models (with outcomes on raw and log-transformed scales, respectively) and three count distribution-based models (i.e., Poisson, negative binomial, and zero-inflated Poisson (ZIP) models). We also considered the marginalized zero-inflated Poisson (MZIP) model, a novel alternative that estimates the effects on overall mean while adjusting for zero-inflation. Extensive simulations were conducted to evaluate their the statistical power and Type I error rate across various data conditions. Results: Under zero-inflation, the Poisson model failed to control the Type I error rate, resulting in higher than expected false positive results. When the intervention effects on the zero (vs. non-zero) and count parts were in the same direction, the MZIP model had the highest statistical power, followed by the linear model with outcomes on raw scale, negative binomial model, and ZIP model. The performance of a linear model with a log-transformed outcome variable was unsatisfactory. When only one of the effects on the zero (vs. non-zero) part and the count part existed, the ZIP model had the highest statistical power. Conclusions: The MZIP model demonstrated better statistical properties in detecting true intervention effects and controlling false positive results for zero-inflated count outcomes. This MZIP model may serve as an appealing analytical approach to evaluating overall intervention effects in studies with count outcomes marked by excessive zeros.

翻译：背景：在健康行为研究中，常出现具有过度零值的计数变量作为结果指标。目前缺乏关于现有统计模型在结果零膨胀时的相对性能的实证数据，尤其是与近期开发的方法进行比较。方法：本模拟研究考察了五种常用的计数结果分析方法，包括两种线性模型（分别基于原始尺度和对数转换尺度的结果变量）和三种基于计数分布的模型（即泊松模型、负二项模型和零膨胀泊松模型）。我们还考虑了边缘化零膨胀泊松模型这一新型替代方法，该模型在调整零膨胀的同时估计对总体均值的影响。通过广泛模拟，评估了在不同数据条件下这些模型的统计功效和第一类错误率。结果：在零膨胀条件下，泊松模型未能控制第一类错误率，导致高于预期的假阳性结果。当对零部分和非零部分以及计数部分的干预效应方向相同时，边缘化零膨胀泊松模型的统计功效最高，其次为基于原始尺度结果的线性模型、负二项模型和零膨胀泊松模型。基于对数转换结果变量的线性模型表现不佳。当仅存在对零部分和非零部分或计数部分的单一效应时，零膨胀泊松模型的统计功效最高。结论：边缘化零膨胀泊松模型在检测真实干预效应和控制零膨胀计数结果的假阳性方面表现出更优的统计特性。对于以过度零值标记的计数结果相关研究，该模型可作为评估总体干预效应的有吸引力的分析方法。