The benefits of using semi-continuous and continuous models to analyze binge eating data: A Monte Carlo investigation.

OBJECTIVE: Change in binge eating is typically a primary outcome for interventions targeting individuals with eating pathology. A range of statistical models exist to handle these types of frequency distributions, but little empirical evidence exists to guide the appropriate choice of statistical model. METHOD: Monte Carlo simulations were used to investigate the utility of semi-continuous models relative to continuous models in various situations relevant to binge eating treatment studies. RESULTS: Semi-continuous models yielded more accurate estimates of the population, while continuous models were higher powered when higher levels of missing data were present. DISCUSSION: The present findings generally support the use of semi-continuous models applied to binge eating data, with total sample sizes of roughly 200 being adequately powered to detect moderate treatment effects. However, models with a significant amount of missing data yielded more favorable power estimates for continuous models.