Citation

Hussong AM, Gottfredson NC, Bauer DJ, Curran PJ, Haroon M, Chandler R, Kahana SY, Delaney JAC, Altice FL, Beckwith CG, Feaster DJ, Flynn PM, Gordon MS, Knight K, Kuo I, Ouellet LJ, Quan VM, Seal DW, Springer SA. Drug Alcohol Depend. 2018; 194: 59-68.

Affiliation

Yale School of Medicine, United States. Electronic address: sandra.springer@yale.edu.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.drugalcdep.2018.10.003

PMID

30412898

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.

Copyright © 2018 Elsevier B.V. All rights reserved.

Language: en

Keywords

Data harmonization; Data pooling; Drinking severity; Integrative data analysis