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Missing data in alcohol clinical trials: a comparison of methods
Kevin A Hallgren1, Katie Witkiewitz
1Department of Psychology , University of New Mexico, Albuquerque, New Mexico.
Alcoholism, Clinical and Experimental Research
|July 30, 2013
Summary
Participant dropout in alcohol trials is common. Assuming missing data equals heavy drinking leads to biased results; multiple imputation and full information maximum likelihood are better methods.
Area of Science:
- Clinical Psychology
- Psychopharmacology
- Biostatistics
Background:
- Participant attrition in alcohol clinical trials is substantial, complicating statistical analysis of treatment effects.
- The common practice of assuming missing data indicates relapse (missing = heavy drinking) has not been rigorously evaluated in alcohol studies.
- This missing data assumption may bias treatment effect estimates in alcohol clinical trials.
Purpose of the Study:
- To evaluate the impact of different missing data handling methods on treatment effect estimates in alcohol clinical trials.
- To compare the performance of common imputation methods against more advanced techniques using simulated data.
- To determine the most appropriate methods for analyzing data from alcohol treatment studies with missing outcome data.
Main Methods:
- Simulated missing data scenarios were generated from the COMBINE study data, manipulating sample size and dropout rates.
- Five methods for treating missing data were compared: complete case analysis (CCA), last observation carried forward (LOCF), missing = heavy drinking, multiple imputation (MI), and full information maximum likelihood (FIML).
- The association between naltrexone treatment and heavy drinking was examined over the first 10 weeks post-treatment under various missing data assumptions.
Main Results:
- Complete case analysis, last observation carried forward, and missing = heavy drinking methods yielded the most biased estimates for treatment effects and standard errors.
- Multiple imputation and full information maximum likelihood demonstrated the least bias in estimating treatment effects and standard errors.
- The choice of missing data handling method significantly impacts the accuracy of treatment effect evaluation in alcohol clinical trials.
Conclusions:
- The assumption that missing data equals heavy drinking produces biased results and should be avoided when evaluating treatment effects in alcohol clinical trials.
- Multiple imputation and full information maximum likelihood are recommended as more robust methods for handling missing data in these studies.
- Accurate statistical analysis of alcohol clinical trials requires careful consideration and appropriate methods for addressing missing data.
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