Harmonizing Cigar Survey Data Across Tobacco Centers of Regulatory Science, Center for Tobacco Products, and Population Assessment of Tobacco and Health Studies: The Cigar Collaborative Research Group.

INTRODUCTION: Cigars are a popular tobacco product of choice for youth and young adults. Despite growing interest in cigar research, there are gaps in the available literature limiting an ability to set evidence-based policies. Too small research samples, the heterogeneity of types of cigars when asking a single question about use, makes analyzing data difficult. Given the Food and Drug Administration's (FDA's) authority granted in 2016 to regulate cigars, and its popularity, data to better understand use and preference for cigars will help FDA set appropriate regulatory policies. METHODS: We harmonized cigar survey data previously collected by five independent tobacco regulatory science survey research projects. Data supplying participants included three Tobacco Centers of Regulatory Science, one Center for Tobacco Products grantee, and data from Population Assessment of Tobacco and Health (PATH) study's public use dataset. RESULTS: Analyzing 92 data variables from across five studies, and applying a rigorous data harmonization protocol, we report findings on 24 key cigar use variables. The step by step protocol for harmonizing is presented. Selected findings showing strict reproducibility across all five studies reveal youth 17-19 years at highest risk for cigar initiation; relative reproducibility shows males more likely to try cigars than females but with significant differences in magnitude across studies; and areas of inconsistent reproducibility are revealed when evaluating brand preferences. CONCLUSION: Harmonizing data from multiple sources fosters a broader view of the robustness and generalizability of survey data than that from a single source. These observations raise awareness to look for the highest degree of reproducibility among and across data sources to inform policy. IMPLICATIONS: Harmonizing data from discrete datasets provides insights into cigar initiation and use and is presented showing opportunities, challenges, and solutions. Comparing observational data from PATH and four independent research studies provides a best-practices approach and example of data synthesis for the tobacco research community. The dataset of five studies offers a look at the degree of confidence in analyzing harmonized survey results. Variable conclusions raise the need to strive for the highest degree of reproducibility, to best understand the behaviors of cigar users, and allow for the future development of the most effective interventions to alter tobacco use patterns.

Approaches for creating comparable measures of alcohol use symptoms: Harmonization with eight studies of criminal justice populations.

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.