Approaches for creating comparable measures of alcohol use symptoms: Harmonization with eight studies of criminal justice populations. (1st January 2019)

• Record Type: Journal Article
• Authors: Hussong, Andrea M.; Gottfredson, Nisha C.; Bauer, Dan J.; Curran, Patrick J.; Haroon, Maleeha; Chandler, Redonna; Kahana, Shoshana Y.; Delaney, Joseph A.C.; Altice, Frederick L.; Beckwith, Curt G.; Feaster, Daniel J.; Flynn, Patrick M.; Gordon, Michael S.; Knight, Kevin; Kuo, Irene; Ouellet, Lawrence J.; Quan, Vu M.; Seal, David W.; Springer, Sandra A.
• Is Part Of: Drug and alcohol dependence. Volume 194(2019)
• Page Start: 59
• Page End: 68
• Publication Date: 2019-01-01
• Subjects: Data pooling -- Drinking severity -- Integrative data analysis -- Data harmonization; Drug abuse -- Periodicals; Alcoholism -- Periodicals; 616.86
• DOI: 10.1016/j.drugalcdep.2018.10.003
• Languages: English
• ISSNs: 0376-8716

Abstract

Highlights: Moderated nonlinear factor analysis is a tool for pooled analysis. MNLFA scores had more desirable properties than pooled cut-scores and sum scores. MNLFA scores showed strongly predictive validity than other scores. MNLFA is a promising tool for harmonization in pooled data analysis.. Abstract: Background: With increasing data archives comprised of studies with similar measurement, optimal methods for data harmonization and measurement scoring are a pressing need. We compare three methods for harmonizing and scoring the AUDIT as administered with minimal variation across 11 samples from eight study sites within the STTR (Seek-Test-Treat-Retain) Research Harmonization Initiative. Descriptive statistics and predictive validity results for cut-scores, sum scores, and Moderated Nonlinear Factor Analysis scores (MNLFA; a psychometric harmonization method) are presented. Methods: Across the eight study sites, sample sizes ranged from 50 to 2405 and target populations varied based on sampling frame, location, and inclusion/exclusion criteria. The pooled sample included 4667 participants (82% male, 52% Black, 24% White, 13% Hispanic, and 8% Asian/ Pacific Islander; mean age of 38.9 years). Participants completed the AUDIT at baseline in all studies. Results: After logical harmonization of items, we scored the AUDIT using three methods: published cut-scores, sum scores, and MNLFA. We found greater variation, fewer floor effects, and the ability to directly address missing data in MNLFA scores as compared to cut-scores and sum scores. MNLFA scores showed stronger associations with binge drinking and clearer study differences than did other scores. Conclusions: MNLFA scores are a promising tool for data harmonization and scoring in pooled data analysis. Model complexity with large multi-study applications, however, may require new statistical advances to fully realize the benefits of this approach.