TY - JOUR
T1 - Approaches for creating comparable measures of alcohol use symptoms
T2 - Harmonization with eight studies of criminal justice populations
AU - Hussong, Andrea M.
AU - Gottfredson, Nisha C.
AU - Bauer, Dan J.
AU - Curran, Patrick J.
AU - Haroon, Maleeha
AU - Chandler, Redonna
AU - Kahana, Shoshana Y.
AU - Delaney, Joseph A.C.
AU - Altice, Frederick L.
AU - Beckwith, Curt G.
AU - Feaster, Daniel J.
AU - Flynn, Patrick M.
AU - Gordon, Michael S.
AU - Knight, Kevin
AU - Kuo, Irene
AU - Ouellet, Lawrence J.
AU - Quan, Vu M.
AU - Seal, David W.
AU - Springer, Sandra A.
N1 - Funding Information:
The opinions in this paper are those of the authors and do not reflect those of the National Institute on Drug Abuse, the National Institutes of Health, or the Department of Health and Human Services. Research reported in this publication is the result of secondary data analysis and was supported by and administrative supplement to 1R01DA034636-01A1 and K01 DA035153 from the National Institute on Drug Abuse . The STTR collaborative was funded by 5U01DA037702 and primary data collection for STTR studies was supported by grants R01DA030771, R01DA030762, 5R01DA030793, R01 DA030768, 5U01DA037702, R01 DA030747, and R01 DA030776. Dr. Chandler was substantially involved in U01 DA037702, consistent with her role as Scientific Officer. She had no substantial involvement in the other cited grants. The authors thank the other investigators, the staff, and particularly the participants of the individual STTR studies for their valuable contributions. A full list of participating STTR investigators and institutions can be found at http://www.sttr-hiv.org . All authors have reviewed this publication and consented for publication.
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - Data harmonization
KW - Data pooling
KW - Drinking severity
KW - Integrative data analysis
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U2 - 10.1016/j.drugalcdep.2018.10.003
DO - 10.1016/j.drugalcdep.2018.10.003
M3 - Article
C2 - 30412898
AN - SCOPUS:85056179571
SN - 0376-8716
VL - 194
SP - 59
EP - 68
JO - Drug and alcohol dependence
JF - Drug and alcohol dependence
ER -