Modeling Cyclical Patterns in Daily College Drinking Data with Many Zeroes.

Daily college drinking data often have highly skewed distributions with many zeroes and a rising and falling pattern of use across the week. Alcohol researchers have typically relied on statistical models with dummy variables for either the weekend or all days of the week to handle weekly patterns of use. However, weekend versus weekday categorizations may be too simplistic and saturated dummy variable models too unwieldy, particularly when covariates of weekly patterns are included. In the present study we evaluate the feasibility of cyclical (sine and cosine) covariates in a multilevel hurdle count model for evaluating daily college alcohol use data. Results showed that the cyclical parameterization provided a more parsimonious approach than multiple dummy variables. The number of drinks when drinking had a smoothly rising and falling pattern that was reasonably approximated by cyclical terms, but a saturated set of dummy variables was a better model for the probability of any drinking. Combining cyclical terms and multilevel hurdle models is a useful addition to the data analyst toolkit when modeling longitudinal drinking with high zero counts. However, drinking patterns were not perfectly sinusoidal in the current application, highlighting the need to consider multiple models and carefully evaluate model fit.