2016
DOI: 10.1111/acer.13106
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Missing Data in Alcohol Clinical Trials with Binary Outcomes

Abstract: Background Missing data are common in alcohol clinical trials for both continuous and binary endpoints. Approaches to handle missing data have been explored for continuous outcomes, yet no studies have compared missing data approaches for binary outcomes (e.g., abstinence, no heavy drinking days). The present study compares approaches to modeling binary outcomes with missing data in the COMBINE study. Method We included participants in the COMBINE Study who had complete drinking data during treatment and who… Show more

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Cited by 28 publications
(17 citation statements)
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References 31 publications
(53 reference statements)
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“…These analyses also involved a subset of the original sample (participants with chronic pain who were retained after BUP-NLX detoxification and maintenance), so these findings may not generalize to the general population of prescription opioid-dependent adults who seek treatment. A potential concern is the accelerated rate of attrition during the taper phase which led to a steep increase in missing data, although confidence in the results is bolstered by our use of modern estimation procedures that are generally more robust to missing data than alternative approaches (39). The original clinical trial was conducted in community clinics connected to an established clinical trials research network, therefore the frequency and quality of clinical services provided in this study may differ from those typically available to this population.…”
Section: Discussionmentioning
confidence: 99%
“…These analyses also involved a subset of the original sample (participants with chronic pain who were retained after BUP-NLX detoxification and maintenance), so these findings may not generalize to the general population of prescription opioid-dependent adults who seek treatment. A potential concern is the accelerated rate of attrition during the taper phase which led to a steep increase in missing data, although confidence in the results is bolstered by our use of modern estimation procedures that are generally more robust to missing data than alternative approaches (39). The original clinical trial was conducted in community clinics connected to an established clinical trials research network, therefore the frequency and quality of clinical services provided in this study may differ from those typically available to this population.…”
Section: Discussionmentioning
confidence: 99%
“…Finally, imputing for missing data may produce biased estimates, which depend upon the missingness pattern of the data and the analytic method used to impute missing data. 4042 Though it is impossible to know the actual reasons for all missing data, sensitivity analyses can provide a test of whether the assumptions of missing completely at random, missing at random, or missing not at random conditions are likely for a given set of analyses. 42 …”
Section: Discussionmentioning
confidence: 99%
“…To address missing data, we used multiple imputation with chained logistic regression equations in all statistical analyses (30), which reduces bias more than other approaches even when some data are not missing at random(31). Twenty imputation sets were generated using Gibbs sampling, incorporating all baseline variables (including baseline SI and SA) to improve prediction accuracy.…”
Section: Methodsmentioning
confidence: 99%