title: Using a factor mixture modeling approach in alcohol dependence in a general population sample.
abstract: Alcohol dependence (AD) is a complex and heterogeneous disorder. The identification of more homogeneous subgroups of individuals with drinking problems and the refinement of the diagnostic criteria are inter-related research goals. They have the potential to improve our knowledge of etiology and treatment effects, and to assist in the identification of risk factors or specific genetic factors. Mixture modeling has advantages over traditional modeling that focuses on either the dimensional or categorical latent structure. The mixture modeling combines both latent class and latent trait models, but has not been widely applied in substance use research. The goal of the present study is to assess whether the AD criteria in the population could be better characterized by a continuous dimension, a few discrete subgroups, or a combination of the two. More than seven thousand participants were recruited from the population-based Virginia Twin Registry, and were interviewed to obtain DSM-IV (Diagnostic and Statistical Manual of Mental Disorder, version IV) symptoms and diagnosis of AD. We applied factor analysis, latent class analysis, and factor mixture models for symptom items based on the DSM-IV criteria. Our results showed that a mixture model with 1 factor and 3 classes for both genders fit well. The 3 classes were a non-problem drinking group and severe and moderate drinking problem groups. By contrast, models constrained to conform to DSM-IV diagnostic criteria were rejected by model fitting indices providing empirical evidence for heterogeneity in the AD diagnosis. Classification analysis showed different characteristics across subgroups, including alcohol-caused behavioral problems, comorbid disorders, age at onset for alcohol-related milestones, and personality. Clinically, the expanded classification of AD may aid in identifying suitable treatments, interventions and additional sources of comorbidity based on these more homogenous subgroups of alcohol use problems.
authors.affiliation: Institute of Clinical Medicine, College of Medicine, National Cheng Kung University, Taiwan. pkuo@mail.ncku.edu.tw
authors.forename: Po-Hsiu
authors.initials: PH
authors.lastname: Kuo
authors.forename: Steven H
authors.initials: SH
authors.lastname: Aggen
authors.forename: Carol A
authors.initials: CA
authors.lastname: Prescott
authors.forename: Kenneth S
authors.initials: KS
authors.lastname: Kendler
authors.forename: Michael C
authors.initials: MC
authors.lastname: Neale
mesh_terms: D000328:Adult; D019973:Alcohol-Related Disorders / Q000175:diagnosis / Q000453:epidemiology / Q000235:genetics; D000437:Alcoholism / Q000175:diagnosis / Q000453:epidemiology* / Q000235:genetics*; D003430:Cross-Sectional Studies; D039721:Diagnostic and Statistical Manual of Mental Disorders; D004200:Diseases in Twins / Q000175:diagnosis / Q000453:epidemiology* / Q000235:genetics*; D005260:Female; D006306:Health Surveys; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015203:Reproducibility of Results; D012307:Risk Factors; D014768:Virginia