2013
DOI: 10.1111/acer.12205
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Missing Data in Alcohol Clinical Trials: A Comparison of Methods

Abstract: 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 bee… Show more

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Cited by 148 publications
(154 citation statements)
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“…This missing data technique is considered superior to other traditional techniques (e.g., pairwise and listwise deletion) because it "maximizes statistical power by borrowing information from the observed data" (Enders, 2010, p. 87). Indeed, recent research has shown that FIML is effective in reducing biases due to selective attrition (e.g., Hallgren & Witkiewitz, 2013).…”
Section: Discussionmentioning
confidence: 99%
“…This missing data technique is considered superior to other traditional techniques (e.g., pairwise and listwise deletion) because it "maximizes statistical power by borrowing information from the observed data" (Enders, 2010, p. 87). Indeed, recent research has shown that FIML is effective in reducing biases due to selective attrition (e.g., Hallgren & Witkiewitz, 2013).…”
Section: Discussionmentioning
confidence: 99%
“…MLM utilises full information maximum likelihood (FIML) estimation, which are optimal for handling missing data (Graham, 2009), which is substantial in treatment studies. FIML produces less biased estimates than other missing data approaches, such as assuming relapse or carrying forward the last observation (Hallgren and Witkiewitz, 2013). Full iterative generalized least squares (IGLS) estimation, a type of FIML, was employed for days used and amount used models.…”
Section: Discussionmentioning
confidence: 99%
“…[pgs. [15][16] 5. Requests more information about model assumptions and how betas were standardized.…”
Section: The Reviewer Requests a Condensing Of The Discussion Around mentioning
confidence: 99%