Psychometric modeling of cannabis initiation and use and the symptoms of cannabis abuse, dependence and withdrawal in a sample of male and female twins.

BACKGROUND: Despite an emerging consensus that the DSM-IV diagnostic criteria for cannabis abuse and dependence are best represented by a single underlying liability, it remains unknown if latent class or hybrid models can better explain the data.
METHOD: Using structured interviews, 7316 adult male and female twins provided complete data on DSM-IV symptoms of cannabis abuse and dependence. Our aim was to derive a parsimonious, best-fitting cannabis use disorder (CUD) phenotype based on DSM-III-R/IV criteria by comparing an array of psychometric models (latent factor analysis, latent class analysis and factor mixture modeling) using full information maximum likelihood ordinal data methods in Mx.
RESULTS: We found little evidence to support population heterogeneity since neither latent class nor hybrid factor mixture models provided a consistently good fit to the data. When conditioned on initiation and cannabis use, the endorsement patterns of the abuse, dependence and withdrawal criteria were best explained by two latent factors for males and females. The first was a general CUD factor for which genetic effects explained 53-54% of the variance. A less interpretable second factor included a mix of cross-loading dependence and withdrawal symptoms.
CONCLUSIONS: This is the first study to compare competing measurement models to derive an empirically determined CUD phenotype. Commensurate with proposed changes to substance use disorders in the DSM-V, our results support an emerging consensus that a single CUD latent factor can more optimally assess the risk or liability underpinning correlated measures of use, abuse, dependence and withdrawal criterion. Published by Elsevier Ireland Ltd.