Evidence map›Paper›PMID 28038361›Full record

Trial reportDrug and alcohol dependence2017

The search for an elusive cutoff remains: Problems of binary classification of heavy drinking as an endpoint for alcohol clinical trials.

Matthew R Pearson, Adrian J Bravo, Megan Kirouac, Katie Witkiewitz

Abstract readMulticenter StudyRandomized Controlled Trial
In one paragraph

Trial report in Drug and alcohol dependence, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. Mifepristone Decreases Chronic Voluntary Ethanol Consumption in Rhesus Macaques.The Journal of pharmacology and experimental therapeutics · 2020
    Article
  8. Article
  9. Article
  10. Flexibly modeling alcohol use disorder risk: How many drinks should we count?Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2019
    Article
  11. Article
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

4 authors.

Matthew R PearsonCenter on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, 2650 Yale Blvd SE MSC 11-6280, Albuquerque, NM 87106, United States. Electronic address: mateo.pearson@gmail.com.
Adrian J BravoCenter on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, 2650 Yale Blvd SE MSC 11-6280, Albuquerque, NM 87106, United States. Electronic address: ajbravo@unm.edu.
Megan KirouacCenter on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, 2650 Yale Blvd SE MSC 11-6280, Albuquerque, NM 87106, United States; Department of Psychology, University of New Mexico, United States. Electronic address: mkirouac@unm.edu.
Katie WitkiewitzCenter on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, 2650 Yale Blvd SE MSC 11-6280, Albuquerque, NM 87106, United States; Department of Psychology, University of New Mexico, United States. Electronic address: katiew@unm.edu.

Funding

Alcohol Research Training: Methods & MechanismsT32AA018108 · NIAAA · UNIVERSITY OF NEW MEXICO · PI Katie A Witkiewitz · 2010 to 2026
$5.6M
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
Efficacy and neurobiological mechanisms of mobile-delivered cognitive retrainingK01AA023233 · NIAAA · UNIVERSITY OF NEW MEXICO · PI PEARSON, MATTHEW RYAN · 2014 to 2018
$832k
Toward Identifying Clinically Useful Definitions of Treatment SuccessF31AA024959 · NIAAA · UNIVERSITY OF NEW MEXICO · PI KIROUAC, MEGAN · 2016 to 2017
$50k
NIAAA NIH HHS F31 AA024959NIAAA NIH HHS K01 AA023233NIAAA NIH HHS R01 AA022328NIAAA NIH HHS T32 AA018108
6 · The paper itself

Abstract

backgroundTo examine whether a clinically meaningful alcohol consumption cutoff can be created for clinical samples, we used receiver operator characteristic (ROC) curves to derive gender-specific consumption cutoffs that maximized sensitivity and specificity in the prediction of a wide range of negative consequences from drinking.

methodsWe conducted secondary data analyses using data from two large clinical trials targeting alcohol use disorders: Project MATCH (n=1726) and COMBINE (n=1383).

resultsIn both studies, we found that the ideal cutoff for men and women that maximized sensitivity/specificity varied substantially both across different alcohol consumption variables and alcohol consequence outcomes. Further, the levels of sensitivity/specificity were poor across all consequences.

conclusionsThese results fail to provide support for a clinically meaningful alcohol consumption cutoff and suggest that binary classification of levels of alcohol consumption is a poor proxy for maximizing sensitivity/specificity in the prediction of negative consequences from drinking. Future research examining consumption-consequence associations should take advantage of continuous measures of alcohol consumption and alternative approaches for assessing the link between levels of consumption and consequences (e.g., ecological momentary assessment). Clinical researchers should consider focusing more directly on the consequences they aim to reduce instead of relying on consumption as a proxy for more clinically meaningful outcomes.

Indexed as

AdultAlcohol DrinkingAlcohol-Related DisordersBinge DrinkingEndpoint DeterminationEthanolFemaleHumansMaleROC CurveEthanolAlcohol related consequencesAlcohol treatment outcomeAlcohol use disordersHeavy drinkingReceiver operating characteristics

Identifiers

PMID28038361
PMCPMC5262813

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.