Evidence map›Paper›PMID 40618108›Full record

ReviewPerioperative medicine (London, England)2025

Efficient statistical analysis of trial designs: win ratio and related approaches for composite outcomes.

Wilson Fandino, Matthew Dodd, Gudrun Kunst, Tim Clayton

Abstract readReview
In one paragraph

Review in Perioperative medicine (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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

4 authors.

Wilson FandinoAnaesthetics Department, Guy's and St Thomas' Hospital NHS Foundation Trust, London, UK.
Matthew DoddClinical Trials Unit, London School of Hygiene and Tropical Medicine, London, UK.
Gudrun KunstDepartment of Anaesthetics, King's College Hospital NHS Foundation Trust and School of Cardiovascular and Metabolic Medicine & Sciences, King's College London, London, UK. gudrun.kunst@kcl.ac.uk.
Tim ClaytonClinical Trials Unit, London School of Hygiene and Tropical Medicine, London, UK. Tim.Clayton@lshtm.ac.uk.

Funding

NIHR NIHR306274
6 · The paper itself

Abstract

In randomized controlled clinical trials, composite outcomes are often used to study treatment effects. This approach is popular because it increases the number of observed events, enhancing statistical power while reducing the required patient sample size. However, composite outcomes do not provide insight into the effect of individual endpoints. This becomes particularly relevant when mortality is combined with less critical but clinically relevant endpoints or when the clinical importance of individual endpoints varies significantly. As a result, interpreting composite outcomes can be challenging.This narrative review introduces the win ratio (WR), a method for prioritizing individual endpoints within a composite outcome. The WR offers an alternative to composite outcomes by considering the clinical importance of each component and prioritizing the most critical endpoint, such as death, over less significant events.Despite the popularity of the WR among cardiovascular trialists, this approach has not been extensively used in other areas of clinical research. We contend, that perioperative and periprocedural researchers could consider the WR and related approaches when the outcomes of interest are not of similar clinical importance. To this end, understanding the benefits and limitations of the WR will be essential to exploit its benefits, while avoiding potential misuses of the technique.

Identifiers

PMID40618108
PMCPMC12229281

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.