Evidence map›Paper›PMID 38519819›Full record

ArticleAddiction (Abingdon, England)2024

Target trial emulation for comparative effectiveness research with observational data: Promise and challenges for studying medications for opioid use disorder.

Paul J Christine, Sara Lodi, Heather E Hsu, Benjamin Bovell-Ammon, Shapei Yan, Dana Bernson, Patricia Novo, Joshua D Lee, John Rotrosen, Jane Liebschutz and 2 more

Abstract read
In one paragraph

Article in Addiction (Abingdon, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Trial
  2. Article
  3. Observational
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
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  10. 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

12 authors.

Paul J ChristineSection of General Internal Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA.ORCID 0000-0003-1707-0761
Sara LodiDepartment of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
Heather E HsuDepartment of Pediatrics, Boston Medical Center and Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Benjamin Bovell-AmmonDepartments of Medicine and Healthcare Delivery and Population Sciences, Baystate Health and University of Massachusetts Chan Medical School - Baystate, Springfield, MA, USA.
Shapei YanSection of General Internal Medicine, Department of Medicine, Boston Medical Center and Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Dana BernsonDepartment of Public Health, Commonwealth of Massachusetts, Boston, MA, USA.
Patricia NovoDepartment of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA.
Joshua D LeeDepartment of Population Health, New York University Grossman School of Medicine, New York, NY, USA.
John RotrosenDepartment of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA.
Jane LiebschutzDivision of General Internal Medicine, Center for Research on Health Care, Department of Medicine, University of Pittsburgh, UPMC, Pittsburgh, PA, USA.
Alexander Y WalleySection of General Internal Medicine, Department of Medicine, Boston Medical Center and Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Marc R LarochelleSection of General Internal Medicine, Department of Medicine, Boston Medical Center and Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.

Funding

Project-003UG1DA013035 · NIDA · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Aimee N Campbell, Jennifer McNeely · 2015 to 2026
$130.3M
CU PARTNERS: NY/LONG ISLAND REGIONAL NODEU10DA013035 · NIDA · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · PI NUNES, EDWARD V., ROTROSEN, JOHN P · 2001 to 2015
$47.6M
MOUD Comparative Effectiveness StudyR01DA054268 · NIDA · BOSTON MEDICAL CENTER · PI LAROCHELLE, MARC · 2021 to 2025
$2.7M
Improving value-based healthcare delivery for opioid use disorderK01DA054328 · NIDA · BOSTON MEDICAL CENTER · PI HSU, HEATHER · 2021 to 2025
$897k
NIDA NIH HHS K01 DA054328NIDA NIH HHS K01DA054328NIDA NIH HHS R01 DA054268NIDA NIH HHS U10 DA013035NIDA NIH HHS U10/UG1 DA013035NIDA NIH HHS UG1 DA013035
6 · The paper itself

Abstract

Medications for opioid use disorder (MOUD) increase retention in care and decrease mortality during active treatment; however, information about the comparative effectiveness of different forms of MOUD is sparse. Observational comparative effectiveness studies are subject to many types of bias; a robust framework to minimize bias would improve the quality of comparative effectiveness evidence. This paper discusses the use of target trial emulation as a framework to conduct comparative effectiveness studies of MOUD with administrative data. Using examples from our planned research project comparing buprenorphine-naloxone and extended-release naltrexone with respect to the rates of MOUD discontinuation, we provide a primer on the challenges and approaches to employing target trial emulation in the study of MOUD.

Indexed as

Buprenorphine, Naloxone Drug CombinationComparative Effectiveness ResearchNaltrexoneNarcotic AntagonistsOpiate Substitution TreatmentOpioid-Related DisordersBuprenorphineDelayed-Action PreparationsHumansNaloxoneObservational Studies as TopicResearch DesignBuprenorphineBuprenorphine, Naloxone Drug CombinationDelayed-Action PreparationsNaloxoneNaltrexoneNarcotic Antagonistscausal inferenceemulated trialobservational researchopioidssubstance usetarget trial emulation

Identifiers

PMID38519819
PMCPMC11156545

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.