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Background: Outcome measures that are count variables with excessive zerosare common in health behaviors research. There is a lack of empirical dataabout the relative performance of prevailing statistical models when outcomesare zero-inflated, particularly compared with recently developed approaches.  Methods: The current simulation study examined five commonly used analyticalapproaches for count outcomes, including two linear models (with outcomes onraw and log-transformed scales, respectively) and three countdistribution-based models (i.e., Poisson, negative binomial, and zero-inflatedPoisson (ZIP) models). We also considered the marginalized zero-inflatedPoisson (MZIP) model, a novel alternative that estimates the effects on overallmean while adjusting for zero-inflation. Extensive simulations were conductedto evaluate their the statistical power and Type I error rate across variousdata conditions.  Results: Under zero-inflation, the Poisson model failed to control the Type Ierror rate, resulting in higher than expected false positive results. When theintervention effects on the zero (vs. non-zero) and count parts were in thesame direction, the MZIP model had the highest statistical power, followed bythe linear model with outcomes on raw scale, negative binomial model, and ZIPmodel. The performance of a linear model with a log-transformed outcomevariable 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 higheststatistical power.  Conclusions: The MZIP model demonstrated better statistical properties indetecting true intervention effects and controlling false positive results forzero-inflated count outcomes. This MZIP model may serve as an appealinganalytical approach to evaluating overall intervention effects in studies withcount outcomes marked by excessive zeros.

A Simulation Study of the Performance of Statistical Models for Count Outcomes with Excessive Zeros