Evidence map›Paper›PMID 36187300›Full record

ArticleDrug and alcohol dependence reports2022

Under-representation of key demographic groups in opioid use disorder trials.

Kara E Rudolph, Matthew Russell, Sean X Luo, John Rotrosen, Edward V Nunes

Abstract read
In one paragraph

Article in Drug and alcohol dependence reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
  4. Article
  5. Journal of the American Statistical Association · 2025
    Article
  6. Observational
  7. Observational
  8. Article
  9. Article
  10. Article
  11. Article
  12. 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

5 authors.

Kara E RudolphDepartment of Epidemiology, Columbia University, 722 W 168th St, Room 522, New York, NY 10032, United States.
Matthew RussellDepartment of Epidemiology, Columbia University, 722 W 168th St, Room 522, New York, NY 10032, United States.
Sean X LuoDepartment of Psychiatry, School of Medicine, Columbia University, and New York State Psychiatric Institute, New York, New York, United States.
John RotrosenDepartment of Psychiatry, New York University Grossman School of Medicine, New York, New York, United States.
Edward V NunesDepartment of Psychiatry, School of Medicine, Columbia University, and New York State Psychiatric Institute, New York, New York, United States.

Funding

Role of disability and pain in opioid overdose: mechanism and risk mitigationR01DA053243 · NIDA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Kara Elizabeth Rudolph · 2022 to 2026
$3.1M
Design and analysis advances to improve generalizability of clinical trials for treating opioid use disorderR01DA056407 · NIDA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Kara Elizabeth Rudolph, Elizabeth A. Stuart · 2022 to 2026
$3.1M
Mechanisms linking neighborhood poverty to problematic adolescent drug useR00DA042127 · NIDA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI RUDOLPH, KARA ELIZABETH · 2018 to 2021
$889k
NIDA NIH HHS R00 DA042127NIDA NIH HHS R01 DA053243NIDA NIH HHS R01 DA056407
6 · The paper itself

Abstract

Background: The extent to which clinical trials of medications for opioid use disorder (MOUD) are representative or not is unknown. Some patient characteristics modify MOUD effectiveness; if these same characteristics differ in distribution between the trial population and usual-care population, this could contribute to lack of generalizability-a discrepancy between trial and usual-care effectiveness. Our objective was to identify interpretable, multidimensional subgroups who were prescribed MOUD in substance use treatment programs in the US but who were not represented or under-represented by clinical trial participants. Methods: This was a secondary descriptive analysis of trial and real-world data. The trial data included twenty-seven US opioid treatment programs in the National Drug Abuse Treatment Clinical Trials Network, N = 2,199 patients. The real-world data included US substance use treatment programs that receive public funding, N = 740,015 patients. We characterized real-world patient populations who were non-represented and under-represented in the trial data in terms of sociodemographic and clinical characteristics that could modify MOUD effectiveness. Results: We found that 10.7% of MOUD patients in TEDS-A were not represented in the three clinical trials. As expected, pregnant MOUD patients (n = 19,490) were not represented. Excluding pregnancy, education and marital status from the characteristics, 2.6% of MOUD patients were not represented. Patients aged 65 years and older (n = 11,204), and those 50-64 years who identified as other (non-White, non-Black, and non-Hispanic) race/ethnicity or multi-racial (n = 7,281) were under-represented. Conclusions: Quantifying and characterizing non- or under-represented subgroups in trials can provide the data necessary to improve representation in future trials and address research-to-practice gaps.

Indexed as

Clinical TrialsExternal validityGeneralizabilityMOUDTrial representation

Identifiers

PMID36187300
PMCPMC9524855

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.