Evidence map›Paper›PMID 41246528›Full record

ArticleClinical epidemiology2025

Leveraging a Bayesian Approach in a Comparative Effectiveness Trial of Major Adverse Cardiovascular Events.

Cara T Lwin, Christianne L Roumie, Robert Alan Greevy, Cole Beck, Kathryn Diane Snyder, Amber J Hackstadt

Abstract read
In one paragraph

Article in Clinical epidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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5 · Who and what money

Authors and funding

6 authors.

Cara T LwinDepartment of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA.
Christianne L RoumieGeriatric Research Education Clinical Center (GRECC) and the VETWISE-LHS Center of Innovation, Veteran Administration Tennessee Valley VA Health Care System, Nashville, TN, USA.ORCID 0000-0002-6555-2055
Robert Alan GreevyDepartment of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA.
Cole BeckDepartment of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA.ORCID 0000-0002-6849-6255
Kathryn Diane SnyderDepartment of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
Amber J HackstadtDepartment of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA.ORCID 0000-0002-0164-6589

Funding

Vanderbilt Center for Diabetes Translation ResearchP30DK092986 · NIDDK · VANDERBILT UNIVERSITY MEDICAL CENTER · PI MILLER-HUGHES, STEPHANIA · 2011 to 2020
$6.7M
CSRD VA I01 CX000570NIDDK NIH HHS P30 DK092986
6 · The paper itself

Abstract

Purpose: We applied a Bayesian approach to further investigate the association of sodium-glucose cotransporter-2 inhibitors (SGLT2i) with the composite outcome of Major Adverse Cardiovascular Event and Heart Failure hospitalization (MACE+HF) and its individual components leveraging the ability of a Bayesian approach to incorporate prior clinical information and to make probability statements about the parameters. Methods: We use a Bayesian time-to-event model, where the covariates are directly modeled in the hazard function. Following propensity score matching, we fit three Bayesian models; one with a relatively flat, normal prior on the SGLT2i coefficient (Uninformative) and 2 with informative priors from a meta-analysis (based on a cohort with no history of cardiovascular disease [No CVD] and cohorts with a history of CVD [CVD]). We estimate the posterior distribution for the hazard ratio (HR) using a Hamiltonian Monte Carlo algorithm. It allows us to estimate the probability of a meaningful protective association (HR < 0.90) in addition to point and interval estimates. Results: The posterior means and 95% credible intervals for the HR suggested a protective association for SGLT2i versus dipeptidyl peptidase 4 inhibitors (DPP4i) for the MACE+HF outcome: No CVD: 0.82 (0.68, 0.96), CVD: 0.82 (0.71, 0.94), and Uninformative: 0.79 (0.65, 0.94). The probability of a meaningful protective association for the No CVD, CVD, and Uninformative priors were 88%, 92%, and 93%, respectively. The probability of a meaningful protective association for the HF hospitalization, CVD hospitalization and CVD death components of MACE+HF were 95%, 67%, and 93%, respectively. Conclusion: The Bayesian analysis allowed for the incorporation of prior information via an informative prior and further investigation of the association between SGLT2 and the components of the MACE+HF composite outcome. It allowed for the calculation of an easily interpretable summary measure, the probability of a meaningful protective association.

Indexed as

bayesian inferencecardiovascular diseasediabetes mellitushamiltonian monte carlopropensity score methodstime-to-event analysis

Identifiers

PMID41246528
PMCPMC12614493

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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.