Evidence map›Paper›PMID 40696103›Full record

ArticleTherapeutic innovation & regulatory science2025

Statistical Considerations and Challenges with Time-to-Event Analyses for Composite Endpoints in Clinical Trials.

Kaiyi Chen, Yu Du, Yuxin Zhu

Abstract read
PubMed Publisher
In one paragraph

Article in Therapeutic innovation & regulatory science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Kaiyi ChenGlobal Statistical Science, Eli Lilly and Company, Indianapolis, Indiana, USA.
Yu DuGlobal Statistical Science, Eli Lilly and Company, Indianapolis, Indiana, USA.
Yuxin ZhuArmstrong Institute for Patient Safety and Quality, Johns Hopkins University, Baltimore, MD, USA. daisy@jhu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of composite endpoint is a common strategy often employed to enhance statistical power and address the low incidence of individual outcomes, particularly in cardiovascular and kidney outcome studies. By merging multiple clinically relevant events into a single variable, these endpoints negate the need for multiple testing adjustments and augment the event rate, thus enabling studies of reasonable size and duration. However, as underscored by the FDA's guidance, a thorough evaluation of each component's impact is equally important to ensure the clinical relevance of these endpoints. This article delves into controversies surrounding the interpretation of hazard ratios derived from analyzing the composite endpoint and its individual components, exemplified by an observation from the CLEAR outcome trials. It highlights a paradoxical scenario where the combined treatment effect for the composite endpoint appeared less favorable than when assessing individual components separately. Moreover, we did a re-evaluation of the suitability of using Cox proportional hazards model in this context through theoretical investigation and simulation studies.

Indexed as

Clinical Trials as TopicEndpoint DeterminationHumansProportional Hazards ModelsClinical trialsComposite endpointProportional hazardsTime-to-event

Identifiers

What OpenQuestion holds

Textmetadata
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.