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，含零过多的计数结果变量十分常见。目前尚缺乏关于现有统计模型在处理零膨胀结果时相对性能的实证数据，尤其是与近期开发的方法相比。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变换后的结果）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，这是一种新颖的替代方法，可在调整零膨胀的同时估计对总体均值的影响。通过广泛的模拟，评估了不同数据条件下这些模型的统计功效和I类错误率。结果：在零膨胀情况下，泊松模型未能控制I类错误率，导致假阳性结果高于预期。当零（相对于非零）部分和计数部分的干预效应方向相同时，MZIP模型具有最高的统计功效，其次是原始尺度线性模型、负二项模型和ZIP模型。对数变换后结果变量的线性模型表现不佳。当仅存在零（相对于非零）部分或计数部分的效应时，ZIP模型具有最高的统计功效。结论：对于零膨胀计数结果，MZIP模型在检测真实干预效应和控制假阳性结果方面表现出更优的统计特性。在含零过多的计数结果研究中，MZIP模型可作为评估总体干预效应的一种有吸引力的分析方法。