TY - JOUR
T1 - 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
AU - Anton In conjunction with the Alcohol Clinical Trials Initiative (ACTIVE) Workgroup
AU - Witkiewitz, Katie
AU - Falk, Daniel E.
AU - Kranzler, Henry R.
AU - Litten, Raye Z.
AU - Hallgren, Kevin A.
AU - O'Malley, Stephanie S.
AU - Anton, Raymond F.
AU - Mann, Karl
AU - McCann, David
AU - Meulien, Didier
AU - Meyer, Roger
AU - O'Brien, Charles
AU - Permutt, Thomas
AU - Robinson, Rebecca
AU - Torup, Lars
AU - Winchell, Celia
AU - Wong, Conrad
AU - Rendenbach-Mueller, Beatrice
AU - Silverman, Bernard
AU - Hammond, Jennifer
AU - Timm, Sarah
N1 - Publisher Copyright:
© 2014 by the Research Society on Alcoholism.
PY - 2014/11/1
Y1 - 2014/11/1
N2 - 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.
AB - 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.
KW - Alcohol use disorder
KW - Clinical trials
KW - Continuous outcome measure
KW - Missing data
KW - Relapse
KW - Treatment
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U2 - 10.1111/acer.12543
DO - 10.1111/acer.12543
M3 - Article
C2 - 25421518
AN - SCOPUS:84911439446
SN - 0145-6008
VL - 38
SP - 2826
EP - 2834
JO - Alcoholism: Clinical and Experimental Research
JF - Alcoholism: Clinical and Experimental Research
IS - 11
ER -