Methods to analyze treatment effects in the presence of missing data for a continuous heavy drinking outcome measure when participants drop out from treatment in alcohol clinical trials

Anton In conjunction with the Alcohol Clinical Trials Initiative (ACTIVE) Workgroup

Research output: Contribution to journalArticle

36 Scopus citations

Abstract

Background: Attrition is common in alcohol clinical trials and the resultant loss of data represents an important methodological problem. In the absence of a simulation study, the drinking outcomes among those who are lost to follow-up are not known. Individuals who drop out of treatment and continue to provide drinking data, however, may be a reasonable proxy group for making inferences about the drinking outcomes of those lost to follow-up. Methods: We used data from the COMBINE study, a multisite, randomized clinical trial, to examine drinking during the 4 months of treatment among individuals who dropped out of treatment but continued to provide drinking data (i.e., "treatment dropouts;" n = 185). First, we estimated the observed treatment effect size for naltrexone versus placebo in a sample that included both treatment completers (n = 961) and treatment dropouts (n = 185; total N = 1,146), as well as the observed treatment effect size among just those who dropped out of treatment (n = 185). In both the total sample (N = 1,146) and the dropout sample (n = 185), we then deleted the drinking data after treatment dropout from those 185 individuals to simulate missing data. Using the deleted data sets, we then estimated the effect of naltrexone on the continuous outcome percent heavy drinking days using 6 methods to handle missing data (last observation carried forward, baseline observation carried forward, placebo mean imputation, missing = heavy drinking days, multiple imputation (MI), and full information maximum likelihood [FIML]). Results: MI and FIML produced effect size estimates that were most similar to the true effects observed in the full data set in all analyses, while missing = heavy drinking days performed the worst. Conclusions: Although missing drinking data should be avoided whenever possible, MI and FIML yield the best estimates of the treatment effect for a continuous outcome measure of heavy drinking when there is dropout in an alcohol clinical trial.

Original languageEnglish (US)
Pages (from-to)2826-2834
Number of pages9
JournalAlcoholism: Clinical and Experimental Research
Volume38
Issue number11
DOIs
StatePublished - Nov 1 2014

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • Toxicology
  • Psychiatry and Mental health

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