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

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This study introduces cyclical covariates in multilevel hurdle models to analyze daily college drinking patterns. Cyclical terms offer a more parsimonious approach than traditional dummy variables for understanding weekly alcohol use trends.

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Area of Science:

  • Statistics
  • Epidemiology
  • Public Health

Background:

  • Daily college drinking data exhibit complex patterns, including high zero counts and weekly cycles.
  • Traditional statistical models using dummy variables for weekdays/weekends may oversimplify weekly alcohol use patterns.
  • More sophisticated modeling is needed to accurately capture the nuances of daily alcohol consumption in college populations.

Purpose of the Study:

  • To evaluate the feasibility of using cyclical (sine and cosine) covariates within a multilevel hurdle count model.
  • To compare the effectiveness of cyclical covariates against traditional dummy variable approaches for modeling daily college alcohol use.
  • To enhance statistical methods for analyzing longitudinal alcohol consumption data with high prevalence of zero counts.

Main Methods:

  • Utilized a multilevel hurdle count model incorporating cyclical covariates (sine and cosine terms).
  • Analyzed daily college alcohol use data, focusing on both the probability of drinking and the number of drinks consumed when drinking.
  • Compared model fit and parsimony between cyclical parameterization and saturated dummy variable models.

Main Results:

  • Cyclical parameterization proved more parsimonious than multiple dummy variables for modeling the number of drinks when drinking.
  • A smoothly rising and falling pattern in drinks consumed was reasonably approximated by cyclical terms.
  • Saturated dummy variables provided a better fit for modeling the probability of any drinking occasion.

Conclusions:

  • Combining cyclical terms with multilevel hurdle models offers a valuable tool for analyzing longitudinal college drinking data with many zero counts.
  • While cyclical terms are effective, college drinking patterns are not perfectly sinusoidal, necessitating consideration of multiple models and careful evaluation of model fit.
  • This approach provides a more refined method for understanding weekly variations in college alcohol consumption compared to simplistic weekend/weekday distinctions.