Evidence map›Paper›PMID 38896944›Full record

ArticleDrug and alcohol dependence2024

Predictors of urine toxicology and other biologic specimen missingness in randomized trials of substance use disorders.

A Taylor Kelley, Michael A Incze, Michael Baumgartner, Aimee N C Campbell, Edward V Nunes, Daniel O Scharfstein

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Drug and alcohol dependence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

A Taylor KelleyDivision of General Internal Medicine, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA; Greater Intermountain Node, National Institute on Drug Abuse Clinical Trial Network, Program of Addiction Research, Clinical Care, Knowledge, and Advocacy (PARCKA), Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA; Informatics, Decision Enhancement, and Analytic Sciences (IDEAS) Center, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; Vulnerable Veteran Patient-Aligned Care Team, VA Salt Lake City Health Care System, Salt Lake City, UT, USA. Electronic address: Alan.Kelley@hsc.utah.edu.
Michael A InczeDivision of General Internal Medicine, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA; Greater Intermountain Node, National Institute on Drug Abuse Clinical Trial Network, Program of Addiction Research, Clinical Care, Knowledge, and Advocacy (PARCKA), Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA.
Michael BaumgartnerDepartment of Philosophy, University of Bergen, Bergen, Norway.
Aimee N C CampbellNew York State Psychiatric Institute, Division on Substance Use Disorders, New York, NY, USA; Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA.
Edward V NunesNew York State Psychiatric Institute, Division on Substance Use Disorders, New York, NY, USA; Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA.
Daniel O ScharfsteinDivision of Biostatistics, Department of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, UT, USA.

Funding

Missing Data Matters: Substance Use Disorder Clinical TrialsR01DA046534 · NIDA · UNIVERSITY OF UTAH · PI SCHARFSTEIN, DANIEL OSCAR · 2018 to 2020
$1.4M
NIDA NIH HHS R01 DA046534
6 · The paper itself

Abstract

backgroundHigh levels of missing outcome data for biologically confirmed substance use (BCSU) threaten the validity of substance use disorder (SUD) clinical trials. Underlying attributes of clinical trials could explain BCSU missingness and identify targets for improved trial design.

methodsWe reviewed 21 clinical trials funded by the NIDA National Drug Abuse Treatment Clinical Trials Network (CTN) and published from 2005 to 2018 that examined pharmacologic and psychosocial interventions for SUD. We used configurational analysis-a Boolean algebra approach that identifies an attribute or combination of attributes predictive of an outcome-to identify trial design features and participant characteristics associated with high levels of BCSU missingness. Associations were identified by configuration complexity, consistency, coverage, and robustness. We limited results using a consistency threshold of 0.75 and summarized model fit using the product of consistency and coverage.

resultsFor trial design features, the final solution consisted of two pathways: psychosocial treatment as a trial intervention OR larger trial arm size (complexity=2, consistency=0.79, coverage=0.93, robustness score=0.71). For participant characteristics, the final solution consisted of two pathways: interventions targeting individuals with poly- or nonspecific substance use OR younger age (complexity=2, consistency=0.75, coverage=0.86, robustness score=1.00).

conclusionsPsychosocial treatments, larger trial arm size, interventions targeting individuals with poly- or nonspecific substance use, and younger age among trial participants were predictive of missing BCSU data in SUD clinical trials. Interventions to mitigate missing data that focus on these attributes may reduce threats to validity and improve utility of SUD clinical trials.

Indexed as

Randomized Controlled Trials as TopicSubstance-Related DisordersFemaleHumansMaleResearch DesignSubstance Abuse DetectionClinical trialConfigurational comparative methodsMissing dataPsychosocial treatmentSubstance use disorderUrine drug screen

Identifiers

PMID38896944
PMCPMC11405181

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Registered trials

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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.