Improving Prediction of Eating-Related Behavioral Outcomes with Zero-Sensitive Regression Models

OBJECTIVE: Outcome variables gauging the frequency of specific disordered eating behaviors (e.g., binge eating, vomiting) are common in the study of eating and health behaviors. The nature of such data presents several analytical challenges, which may be best addressed through the application of underutilized statistical approaches. The current study examined several approaches to predicting count-based behaviors, including zero-sensitive (i.e., zero-inflated and hurdle) regression models. METHOD: Exploration of alternative models to predict eating-related behaviors occurred in two parts. In Part 1, participants (N = 524; 54% female) completed the Eating Disorder Examination-Questionnaire and Daily Stress Inventory. We considered the theoretical basis and practical utility of several alternative approaches for predicting the frequency of binge eating and compensatory behaviors, including ordinary least squares (OLS), logistic, Poisson, negative binomial, and zero-sensitive models. In Part 2, we completed Monte Carlo simulations comparing negative binomial, zero-inflated negative binomial, and negative binomial hurdle models to further explore when these models are most useful. RESULTS: Traditional OLS regression models were generally a poor fit for the data structure. Zero-sensitive models, which are not limited to traditional distribution assumptions, were preferable for predicting count-based outcomes. In the data presented, zero-sensitive models were useful in modeling behaviors that were relatively rare (laxative use and vomiting, 9.7% endorsed) along with those that were somewhat common (binge eating, 33.4% endorsed; driven exercise, 40.7% endorsed). Simulations indicated missing data, sample size, and the number of zeros may impact model fit. DISCUSSION: Zero-sensitive approaches hold promise for answering key questions about the presence and frequency of common eating-related behaviors and improving the specificity of relevant statistical models. Hurdle models may also be appropriate when theoretically justified.