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，结果变量常表现为存在大量零值的计数变量。当结果存在零膨胀现象时，现有统计模型（尤其是与近期新方法相比）的相对性能仍缺乏实证数据。方法：本研究通过模拟实验检验了五种常用计数结果分析方法，包括两种线性模型（分别采用原始尺度和对数转换尺度的结果变量）以及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——这一新型替代方法可在调整零膨胀的同时估计对总体平均值的影响。通过大规模模拟实验，评估了不同数据条件下各模型的统计检验力和第一类错误率。结果：在零膨胀条件下，泊松模型未能控制第一类错误率，导致假阳性结果高于预期。当干预对零值（与非零值）部分和计数部分的影响方向一致时，MZIP模型具有最高的统计检验力，其次为原始尺度线性模型、负二项模型和ZIP模型。采用对数转换结果变量的线性模型性能欠佳。当仅存在对零值（与非零值）部分或计数部分的影响时，ZIP模型统计检验力最高。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展现出更优的统计特性。在存在大量零值的计数结果研究中，MZIP模型可作为评估总体干预效应的理想分析方法。