  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.
