Some considerations for excess zeroes in substance abuse research.

Count data collected in substance abuse research often comes with an excess of “zeroes,” which are typically handled using zero-inflated regression models. However, there is a need to consider the design aspects of those studies before using such a statistical model to ascertain the sources of the zeroes. This study sought to illustrate hurdle models as alternatives to zero-inflated models to validate a two-stage decision-making process in situations of “excess zeroes.” Researchers used data from a study of 45 cocaine-dependent subjects where the primary scientific question was to evaluate whether study participation influences drug-seeking behavior. The outcome, “the frequency (count) of cocaine use days per week,” is bounded (ranging from 0 to 7). The researchers fit and compare binomial, Poisson, negative binomial, and the hurdle version of these models to study the effect of gender, age, time, and study participation on cocaine use, discovering that the hurdle binomial model provides the best fit. Gender and time are not predictive of use. Higher odds of use versus no use are associated with age; however once use is experienced, odds of further use decrease with increase in age. Participation was associated with higher odds of no-cocaine use; once there is use, participation reduced the odds of further use.

Conclusions: Age and study participation are significantly predictive of cocaine-use behavior. The two-stage decision process as modeled by a hurdle binomial model (appropriate for bounded count data with excess zeroes) provides interesting insights into the study of covariate effects on count responses of substance use, when all enrolled subjects are believed to be “at-risk” of use. Nevertheless, the methodological issues discussed here should be a guiding force while considering analysis of “excess zero” situations for bounded or unbounded counts in clinical trials such as the CTN, as well as longitudinal studies on substance use like the one presented.

Categories: Cocaine, Research design, Statistical models
Tags: Article (Peer-Reviewed)
Authors: Bandyopadhyay, Dipankar; DeSantis, Stacia M.; Korte, Jeffrey E.; Brady, Kathleen T.
PMCID: PMC3297079
PMID: 21854280
Source: American Journal of Drug and Alcohol Abuse 2011;37(5):376-382