Missing Data in Alcohol Clinical Trials: A Comparison of Methods

BACKGROUND: The rate of participant attrition in alcohol clinical trials is often substantial and can cause significant issues with regard to the handling of missing data in statistical analyses of treatment effects. It is common for researchers to assume that missing data is indicative of participant relapse, and under that assumption, many researchers have relied on setting all missing values to the worst-case scenario for the outcome (e.g., missing = heavy drinking). This sort of single-imputation method has been criticized for producing biased results in other areas of clinical research, but has not been evaluated within the context of alcohol clinical trials, and many alcohol researchers continue to use the missing = heavy drinking assumption. METHODS: Data from the COMBINE study, a multisite randomized clinical trial, were used to generate simulated situations of missing data under a variety of conditions and assumptions. We manipulated the sample size (n = 200, 500, and 1,000) and dropout rate (5, 10, 25, 30%) under 3 missing data assumptions (missing completely at random, missing at random, and missing not at random). We then examined the association between receiving naltrexone and heavy drinking during the first 10 weeks following treatment using 5 methods for treating missing data (complete case analysis CCA, last observation carried forward LOCF, missing = heavy drinking, multiple imputation MI, and full information maximum likelihood FIML). RESULTS: CCA, LOCF, and missing = heavy drinking produced the most biased naltrexone effect estimates and standard errors under conditions that are likely to exist in randomized clinical trials. MI and FIML produced the least biased naltrexone effect estimates and standard errors. CONCLUSIONS: Assuming that missing = heavy drinking produces biased results of the treatment effect and should not be used to evaluate treatment effects in alcohol clinical trials.