Evidence map›Paper›PMID 35345957›Full record

ArticleStatistical methods in medical research2022

On recurrent-event win ratio.

Lu Mao, KyungMann Kim, Yi Li

Abstract read
In one paragraph

Article in Statistical methods in medical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

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  10. Dissecting the restricted mean time in favor of treatment.Journal of biopharmaceutical statistics · 2024
    Article
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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

3 authors.

Lu MaoDepartment of Biostatistics and Medical Informatics, 5228University of Wisconsin-Madison, USA.ORCID 0000-0002-8626-9822
KyungMann KimDepartment of Biostatistics and Medical Informatics, 5228University of Wisconsin-Madison, USA.
Yi LiDepartment of Biostatistics and Medical Informatics, 5228University of Wisconsin-Madison, USA.

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

The win ratio approach proposed by Pocock et al. (2012) has become a popular tool for analyzing composite endpoints of death and non-fatal events like hospitalization. Its standard version, however, draws on the non-fatal event only through the first occurrence. For statistical efficiency and clinical interpretability, we construct and compare different win ratio variants that make fuller use of recurrent events. We pay special attention to a variant called last-event-assisted win ratio, which compares two patients on the cumulative frequency of the non-fatal event, with ties broken by the time of its latest episode. It is shown that last-event-assisted win ratio uses more data than the standard win ratio does but reduces to the latter when the non-fatal event occurs at most once. We further prove that last-event-assisted win ratio rejects the null hypothesis with large probability if the treatment stochastically delays all events. Simulations under realistic settings show that the last-event-assisted win ratio test consistently enjoys higher power than the standard win ratio and other competitors. Analysis of a real cardiovascular trial provides further evidence for the practical advantages of the last-event-assisted win ratio. Finally, we discuss future work to develop meaningful effect size estimands based on the extended rules of comparison. The R-code for the proposed methods is included in the package WR openly available on the Comprehensive R Archive Network.

Indexed as

HospitalizationHumansCardiovascular trialscomposite endpointsprioritized outcomesstochastic orderstratified analysisU-statistics

Identifiers

PMID35345957
PMCPMC9246892

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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.