Evidence map›Paper›PMID 34799398›Full record

ArticleJournal for immunotherapy of cancer2021

Moving beyond conventional stratified analysis to assess the treatment effect in a comparative oncology study.

Ryan Sun, Zachary McCaw, Lu Tian, Hajime Uno, Fangxin Hong, Dae Hyun Kim, Lee-Jen Wei

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal for immunotherapy of cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

7 authors.

Ryan Sun *Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.ORCID 0000-0003-1176-1561
Zachary McCaw *Insitro, South San Francisco, California, USA.
Lu TianDepartment of Biomedical Data Science, Stanford University, Stanford, California, USA.
Hajime UnoDepartment of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
Fangxin HongDepartment of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
Dae Hyun KimHinda and Arthur Marcus Institute for Aging Research, Harvard Medical School, Boston, Massachusetts, USA.
Lee-Jen WeiDepartment of Biostatistics, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA wei@hsph.harvard.edu.

Funding

Statistical Methods for Optimizing Personalized Treatment SelectionR01HL089778 · NHLBI · STANFORD UNIVERSITY · PI LU TIAN · 2008 to 2026
$4.8M
NHLBI NIH HHS R01 HL089778
6 · The paper itself

Abstract

In a comparative oncology study with progression-free or overall survival as the endpoint, the primary or key secondary analysis is routinely stratified by patients' baseline characteristics when evaluating the treatment difference. The validity of a conventional strategy such as a stratified HR analysis depends on stringent model assumptions that are unlikely to be met in practice, especially in immunotherapy studies. Thus, the resulting summary is generally neither valid nor interpretable. This article discusses issues with conventional stratified analyses and presents alternatives using data from KEYNOTE-189, a recent immunotherapy trial for treating patients with metastatic, non-squamous, non-small-cell lung cancer.

Indexed as

Clinical Trials as TopicHumansImmunotherapyMedical Oncologybiostatisticsclinical trials as topicimmunotherapy

Identifiers

PMID34799398
PMCPMC8606770

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.