Evidence map›Paper›PMID 40734691›Full record

ArticleStatistics & probability letters2025

Approximating win-loss probabilities based on the overall and event-free survival functions.

Lu Mao

Abstract read
In one paragraph

Article in Statistics & probability letters, 2025. 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

1 author.

Lu MaoDepartment of Biostatistics and Medical Informatics, University of Wisconsin-Madison, 610 Walnut St, Room 207A, Madison, WI 53726, USA.ORCID 0000-0002-8626-9822

Funding

Novel Statistical Methods for Complex Time-to-Event Data in Cardiovascular Clinical TrialsR01HL149875 · NHLBI · UNIVERSITY OF WISCONSIN-MADISON · PI Lu Mao · 2020 to 2026
$2.4M
NHLBI NIH HHS R01 HL149875
6 · The paper itself

Abstract

Despite its growing popularity for hierarchical composite endpoints, the win ratio poses a challenge for meta-analysis, as earlier studies typically do not report such measures. In the absence of subject-level data, we show how to approximate it using component-specific Kaplan-Meier curves that are almost universally reported. Given these marginal distributions, we infer the between-component association, as measured by the cross ratio, using summary data on event counts and rates. This leads to approximations of the win-loss probabilities that align closely with raw data-based estimates, as demonstrated in simulations and two case studies. The methodology is implemented in the winkm R package, publicly available on GitHub at https://lmaowisc.github.io/winkm.

Indexed as

Cross ratioHierarchical composite endpointsIdentifiabilityKaplan-Meier curvesMeta-analysisWin ratio

Identifiers

PMID40734691
PMCPMC12302770

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.