Our goal was to classify individuals, not symptoms and hence, we employed a latent class approach rather than a factor analysis model defining one continuous latent trait. It is noteworthy however that the pattern of LCA results obtained here (high, moderate, low, versus subtypes) are typical of data that are well-suited to factor analysis. The use of latent class analysis has successfully been employed to examine patterns of alcohol abuse and dependence symptoms (Beseler et al., 2012; Bucholz et al., 1996; Ko et al., 2010; Moss et al., 2008) as well as other addictions such as nicotine dependence (Agrawal et al., 2011), and opioid dependence (Shand et al., 2011). The majority of these studies also found a 3-class model was the best fit, with the classes representing a severity range from low-risk to high-risk. There are several advantages to examining individuals according to their risk profile, including identifying individuals in a trajectory towards future dependence, distinguishing the best clinical or pharmacological treatment for moderate or at-risk individuals, and -- importantly for this study – reducing genetic and phenotypic heterogeneity.
