Evidence map›Paper›PMID 38638853›Full record

ReviewFrontiers in oncology2024

Competing risks data in clinical oncology.

Haesook Teresa Kim

Abstract readReview
In one paragraph

Review in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Uncertainties in outcome modelling in radiation oncology.Physics and imaging in radiation oncology · 2025
    Review
  12. 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

1 author.

Haesook Teresa KimDepartment of Data Science, Dana-Farber Cancer Institute, Boston, MA, United States.

Funding

Single Cell Analysis and ImmunogeneticsP01HL158505 · NHLBI · DANA-FARBER CANCER INST · PI Corey S Cutler · 2022 to 2026
$15.3M
Single Cell and ImmunogenomicsP01CA229092 · NCI · DANA-FARBER CANCER INST · PI RITZ, JEROME · 2019 to 2023
$13.8M
NCI NIH HHS P01 CA229092NHLBI NIH HHS P01 HL158505
6 · The paper itself

Abstract

Competing risks data analysis plays a critical role in the evaluation of clinical utility of specific cancer treatments and can inform the development of future treatment approaches. Although competing risks data are ubiquitous in cancer studies, competing risks data are infrequently recognized and competing risks data analysis is not commonly performed. Consequently, efficacy of specific treatments is often incompletely and inaccurately presented and thus study results may be interpreted improperly. In the present article, we aim to enhance awareness of competing risks data and provide a general overview and guidance on competing risks data and its analysis using cancer clinical studies.

Indexed as

cancer treatmentclinical utilitycompeting risksdata analysisefficacy

Identifiers

PMID38638853
PMCPMC11024219

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.