Evidence map›Paper›PMID 25421518›Full record

Trial reportAlcoholism, clinical and experimental research2014

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.

Katie Witkiewitz, Daniel E Falk, Henry R Kranzler, Raye Z Litten, Kevin A Hallgren, Stephanie S O'Malley, Raymond F Anton, Alcohol Clinical Trials Initiative (ACTIVE) Workgroup

Registry-linked trialAbstract readMulticenter StudyRandomized Controlled Trial
In one paragraph

Trial report in Alcoholism, clinical and experimental research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06566976 (Prehabilitation and Rehabilitation in PAD), which is not on this map. Cited by 41 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06566976 narecruitingnot on this mapstarted 2024, after this paper: background citation

Prehabilitation and Rehabilitation in PAD: A Randomized Exercise Intervention Trial (PREPARE-IT)

TypeinterventionalSponsorPalo Alto Veterans Institute for ResearchRan2024 to 2027Enrolled300ConditionsPeripheral Arterial DiseaseArmsExercise Intervention, VAPAHCS Rehabilitation Core Components
3 · Its place in the literature

Who cites it

41 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  4. Evaluating a longitudinal mediation model of perceived stress, depressive symptoms, and substance use treatment outcomes.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2020
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  16. Missing Data Sensitivity Analyses for Alcohol Research.Alcohol, clinical & experimental research · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Katie WitkiewitzDepartment of Psychology, University of New Mexico, Albuquerque, New Mexico.
Daniel E Falk
Henry R Kranzler
Raye Z Litten
Kevin A Hallgren
Stephanie S O'Malley
Raymond F Anton
Alcohol Clinical Trials Initiative (ACTIVE) Workgroup

Funding

Precision Medicine and Recovery in a Telehealth Treatment Program for Alcohol Use DisorderR01AA022328 · NIAAA · UNIVERSITY OF NEW MEXICO · PI Katie A Witkiewitz · 2013 to 2026
$3.1M
Career Development and Mentoring in Clinical/Translational Alcohol ResearchK05AA017435 · NIAAA · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI ANTON, RAYMOND F · 2008 to 2017
$2.2M
NIAAA NIH HHS K05 AA017435NIAAA NIH HHS R01 AA022328NIAAA NIH HHS R01 AA022328-01
6 · The paper itself

Abstract

backgroundAttrition 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.

methodsWe 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]).

resultsMI 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.

conclusionsAlthough 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.

Indexed as

Patient DropoutsAdultAlcohol DrinkingAlcoholismFemaleFollow-Up StudiesHumansMaleMiddle AgedResearch DesignStatistics as TopicTreatment OutcomeAlcohol Use DisorderClinical TrialsContinuous Outcome MeasureMissing dataRelapseTreatment

Identifiers

PMID25421518
PMCPMC4244651

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.