Evidence map›Paper›PMID 42534386›Full record

ArticleJournal of clinical and translational science2026

Statistical modeling of days alive out-of-hospital: An illustration using the SSU trial.

Xin Wu, David A Berger, Jason J Bischof, Douglas Char, Deborah Diercks, Erik P Hess, Alan E Jones, Kathleen A Lane, Phillip Levy, Xiaochun Li and 7 more

Abstract read
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In one paragraph

Article in Journal of clinical and translational science, 2026. 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

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

17 authors.

Xin WuDepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.ORCID https://orcid.org/0009-0003-5375-3778
David A BergerDepartment of Emergency Medicine, William Beaumont Hospital, Royal Oak, MI, USA.
Jason J BischofDepartment of Emergency Medicine, The Ohio State University College of Medicine, Columbus, OH, USA.
Douglas CharDepartment of Emergency Medicine, Washington University in St Louis, St Louis, MO, USA.
Deborah DiercksDepartment of Emergency Medicine, The University of Texas Southwestern Medical Center, Dallas, TX, USA.
Erik P HessDepartment of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
Alan E JonesDepartment of Emergency Medicine, The University of Mississippi Medical Center, Jackson, MS, USA.
Kathleen A LaneDepartment of Biostatistics and Health Data Science, Indiana University School of Medicine,, Indianapolis, IN, USA.
Phillip LevyDepartment of Emergency Medicine and Integrative Biosciences Center, Wayne State University, Detroit, MI, USA.
Xiaochun LiDepartment of Biostatistics and Health Data Science, Indiana University School of Medicine,, Indianapolis, IN, USA.
Joseph B MillerDepartment of Emergency Medicine, Henry Ford Health System, Detroit, MI, USA.
Simon A MahlerDepartment of Emergency Medicine, Wake Forest University School of Medicine, Winston Salem, NC, USA.
Susan J PresslerIndiana University School of Nursing, Indianapolis, IN, USA.
Arvind VenkatDepartment of Emergency Medicine, Allegheny Health Network, Pittsburgh, PA, USA.
Peter S PangIndiana University School of Medicine, Indianapolis, IN, USA.
Sean CollinsDepartment of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
Bryan S BletteDepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Days alive out-of-hospital (DAOOH) is a well-accepted outcome in clinical research, as it summarizes both hospitalization and mortality. However, its distribution is often skewed, making accurate modeling challenging in practice. We utilized data from a randomized trial to discuss estimands and statistical methods of interest for DAOOH. Methods: We examined several estimands and statistical models targeting these estimands, including linear regression, gamma regression, ordinal regression, and the probabilistic index model. Comparison was based on interpretability, precision, and model fit. We adopted G-computation to assess causal effects leveraging different regression models to target a simple unified estimand. Results: For unadjusted analyses, the Mann-Whitney Conclusions: Our findings illustrate trade-offs of estimands and model choices for DAOOH, providing guidance for method selection. Based on data from this randomized trial, the Mann-Whitney

Indexed as

cardiovascular healthdays alive out of HospitalG-computationordinal regressionskewed data

Identifiers

PMID42534386
PMCPMC13420199

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Registered trials

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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.