Evidence map›Paper›PMID 39868475›Full record

ArticleInternational journal of epidemiology2024

Four targets: an enhanced framework for guiding causal inference from observational data.

Haidong Lu, Fan Li, Catherine R Lesko, David S Fink, Kara E Rudolph, Michael O Harhay, Christopher T Rentsch, David A Fiellin, Gregg S Gonsalves

Abstract read
In one paragraph

Article in International journal of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Haidong LuDepartment of Internal Medicine, Yale School of Medicine, New Haven, CT, United States.ORCID 0000-0002-7908-2517
Fan LiDepartment of Biostatistics, Yale School of Public Health, New Haven, CT, United States.ORCID 0000-0001-6183-1893
Catherine R LeskoDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0002-1717-2592
David S FinkNew York State Psychiatric Institute, New York, NY, United States.ORCID 0000-0003-1531-1525
Kara E RudolphDepartment of Epidemiology, Columbia Mailman School of Public Health, New York, NY, United States.ORCID 0000-0002-9417-7960
Michael O HarhayDepartment of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States.ORCID 0000-0002-0553-674X
Christopher T RentschDepartment of Internal Medicine, Yale School of Medicine, New Haven, CT, United States.
David A FiellinDepartment of Internal Medicine, Yale School of Medicine, New Haven, CT, United States.
Gregg S GonsalvesProgram in Addiction Medicine, Yale School of Medicine, New Haven, CT, United States.ORCID 0000-0002-5789-9841

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Making Better Decisions: Policy Modeling for AIDS and Drug AbuseR37DA015612 · NIDA · STANFORD UNIVERSITY · PI DOUGLAS K OWENS · 2019 to 2026
$6.3M
Advancing the design, analysis, and interpretation of acute respiratory distress syndrome trials using modern statistical toolsR01HL168202 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI Michael Oscar Harhay, Fan Li · 2023 to 2026
$2.9M
Evaluating and Optimizing Care for Opioid Use Disorder using a Structured Data-Science ApproachR00DA057487 · NIDA · YALE UNIVERSITY · PI Haidong Lu · 2025 to 2026
$497k
Evaluating and Optimizing Care for Opioid Use Disorder using a Structured Data-Science ApproachK99DA057487 · NIDA · YALE UNIVERSITY · PI LU, HAIDONG · 2023 to 2024
$327k
COVID-19 Pandemic: Natural Experiment in Telehealth on Buprenorphine Treatment in a Large Integrated Healthcare SystemK99DA055724 · NIDA · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · PI FINK, DAVID STANLEY · 2023 to 2023
$174k
NCATS NIH HHS UL1 TR001863NHLBI NIH HHS R01 HL168202NIDA NIH HHS K99 DA055724NIDA NIH HHS K99 DA057487NIDA NIH HHS R00 DA057487NIDA NIH HHS R37 DA015612NIH HHS K99DA057487
6 · The paper itself

Abstract

Observational studies play an increasingly important role in estimating causal effects of a treatment or an exposure, especially with the growing availability of routinely collected real-world data. To facilitate drawing causal inference from observational data, we introduce a conceptual framework centered around "four targets"-target estimand, target population, target trial, and target validity. We illustrate the utility of our proposed "four targets" framework with the example of buprenorphine dosing for treating opioid use disorder, explaining the rationale and process for employing the framework to guide causal thinking from observational data. The "four targets" framework is beneficial for those new to epidemiologic research, enabling them to grasp fundamental concepts and acquire the skills necessary for drawing reliable causal inferences from observational data.

Indexed as

BuprenorphineCausalityObservational Studies as TopicOpioid-Related DisordersHumansOpiate Substitution TreatmentBuprenorphinecausal inferenceestimandobservational datatarget populationtarget trialtarget validity

Identifiers

PMID39868475
PMCPMC11769716

What Socratic holds

Textmetadata
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 Socratic graph.