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Comparing single and multiple imputation strategies for harmonizing substance use data across HIV-related cohort
Marjan Javanbakht1, Johnny Lin2, Amy Ragsdale3
1Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA. javan@g.ucla.edu.
BMC Medical Research Methodology
|April 4, 2022
Summary
Multiple imputation (MI) effectively handles missing substance use data in harmonized studies. Single imputation is also effective for high-prevalence drugs with low missingness.
Area of Science:
- Epidemiology
- Biostatistics
- Data Science
Background:
- Standardized substance use measures are often modified, hindering data harmonization across studies.
- Missing data from these modifications pose challenges for accurate analysis.
Purpose of the Study:
- To evaluate the performance of various imputation strategies for missing substance use data.
- To address challenges in harmonizing substance use data from disparate sources.
Main Methods:
- Utilized self-reported substance use data from 528 participants (2,389 visits) in a substance use and HIV cohort.
- Simulated missing data (10-50%) for low (heroin), medium (methamphetamine), and high (cannabis) prevalence drugs.
- Compared single and multiple imputation (MI) using Monte Carlo simulations, assessing bias, RMSE, and confidence interval coverage.
Main Results:
- Complete case analysis (no imputation) significantly underestimated substance use, particularly for low-prevalence drugs like heroin.
- Multiple imputation (MI) demonstrated the least bias across various missingness scenarios.
- Single imputation performed comparably to MI for high-prevalence drugs (cannabis) with low missingness.
Conclusions:
- Multiple imputation (MI) is the preferred method for addressing missing substance use data during harmonization.
- Single imputation offers a viable alternative when outcome prevalence is high and missingness is low.
- These findings offer practical guidance for handling missing data in multi-study substance use research.
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