Evidence map›Paper›PMID 20234363›Full record

ArticleBritish journal of cancer2010

Flexible modeling improves assessment of prognostic value of C-reactive protein in advanced non-small cell lung cancer.

B Gagnon, M Abrahamowicz, Y Xiao, M-E Beauchamp, N MacDonald, G Kasymjanova, H Kreisman, D Small

Open access · hybridAbstract read
In one paragraph

Article in British journal of cancer, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 4 pooled it
2.3field-weighted citation impact, top 12% of its field
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

17 citing papers in PubMed, 4 syntheses or guidelines pooled it, 47 citations in OpenAlex.

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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

8 authors at 2 institutions in 1 country.

B GagnonDepartment of Medicine and Oncology, McGill University, 687 Pine Avenue West, R4.29, Montreal, Quebec, H3A 1A1, Canada. bruno.gagnon@clinepi.mcgill.ca
M Abrahamowicz
Y Xiao
M-E Beauchamp
N MacDonald
G Kasymjanova
H Kreisman
D Small
McGill University · CAJewish General Hospital · CA

Funding

Canadian Institutes of Health Research MOP-8127
6 · The paper itself

Abstract

backgroundC-reactive protein (CRP) is gaining credibility as a prognostic factor in different cancers. Cox's proportional hazard (PH) model is usually used to assess prognostic factors. However, this model imposes a priori assumptions, which are rarely tested, that (1) the hazard ratio associated with each prognostic factor remains constant across the follow-up (PH assumption) and (2) the relationship between a continuous predictor and the logarithm of the mortality hazard is linear (linearity assumption).

methodsWe tested these two assumptions of the Cox's PH model for CRP, using a flexible statistical model, while adjusting for other known prognostic factors, in a cohort of 269 patients newly diagnosed with non-small cell lung cancer (NSCLC).

resultsIn the Cox's PH model, high CRP increased the risk of death (HR=1.11 per each doubling of CRP value, 95% CI: 1.03-1.20, P=0.008). However, both the PH assumption (P=0.033) and the linearity assumption (P=0.015) were rejected for CRP, measured at the initiation of chemotherapy, which kept its prognostic value for approximately 18 months.

conclusionOur analysis shows that flexible modeling provides new insights regarding the value of CRP as a prognostic factor in NSCLC and that Cox's PH model underestimates early risks associated with high CRP.

Indexed as

AgedBiomarkers, TumorCarcinoma, Non-Small-Cell LungC-Reactive ProteinFemaleHumansLung NeoplasmsMaleMiddle AgedMultivariate AnalysisOdds RatioPrognosisProportional Hazards ModelsSurvival AnalysisBiomarkers, TumorC-Reactive Protein

Identifiers

PMID20234363
PMCPMC2853092
OpenAlexW2007533011

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.