Trial report in Clinical pharmacology and therapeutics, 2023. 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
1.1field-weighted citation impact, top 12% of its field
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, 2 citations in OpenAlex.
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
8 authors at 1 institution in 1 country.
HoJin ShinDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0002-2032-6134
Shirley V WangDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0001-7761-7090
Dae Hyun KimDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Ethan AltDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Mufaddal MahesriDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Lily G BessetteDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0003-1088-8579
Sebastian SchneeweissDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0003-2575-467X
Mehdi NajafzadehDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Brigham and Women's Hospital · US
Funding
Randomized Cardiovascular Trials Duplicated Using Prospective Longitudinal Insurance Claims: Applying Techniques of Epidemiology (RCT DUPLICATE)R01HL141505 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI SCHNEEWEISS, SEBASTIAN G., WANG, SHIRLEY · 2019 to 2023
$3.6M
New approaches to safety monitoring of novel systemic treatments for atopic dermatitis in clinical practice and underrepresented populationsR01AR080194 · NIAMS · BRIGHAM AND WOMEN'S HOSPITAL · PI Sebastian G. Schneeweiss · 2022 to 2026
$3.3M
Generalizing data from randomized trials to predict long-term treatment outcomes in older populationsR01AG060163 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI WANG, SHIRLEY · 2018 to 2022
$2.5M
Understanding effectiveness of new drugs in older adults shortly after market entryR01AG053302 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI WANG, SHIRLEY · 2018 to 2021
$2.0M
Mid-Career Mentoring Award For Patient-Oriented Research in Frailty and Health OutcomesK24AG073527 · NIA · HEBREW REHABILITATION CENTER FOR AGED · PI Dae Hyun Kim · 2022 to 2026
Trial results may not be generalizable to target populations treated in clinical practice with different distributions of baseline characteristics that modify the treatment effect. We used outcome models developed with trial data to predict treatment effects in Medicare populations. We used data from the Randomized Evaluation of Long-Term Anticoagulation Therapy trial (RE-LY), which investigated the effect of dabigatran vs. warfarin on stroke or systemic embolism (stroke/SE) among patients with atrial fibrillation. We developed outcome models by fitting proportional hazards models in trial data. Target populations were trial-eligible Medicare beneficiaries who initiated dabigatran or warfarin in 2010-2011 ("early") and 2010-2017 ("extended"). We predicted 2-year risk ratios (RRs) and risk differences (RDs) for stroke/SE, major bleeding, and all-cause death in the Medicare populations using the observed baseline characteristics. The trial and early target populations had similar mean (SD) CHADS
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.
Predicting Treatment Effects of a New-to-Market Drug in Clinical Practice Based on Phase III Randomized Trial Results. · full record | OpenQuestion