ReviewClinical cancer research : an official journal of the American Association for Cancer Research2016
Quantifying Treatment Benefit in Molecular Subgroups to Assess a Predictive Biomarker.
Review in Clinical cancer research : an official journal of the American Association for Cancer Research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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.
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Who cites it
6 citing papers in PubMed.
- Genomic signatures define three subtypes of EGFR-mutant stage II-III non-small-cell lung cancer with distinct adjuvant therapy outcomes.Nature communications · 2021Trial
- Genetic variants of FER and SULF1 in the fibroblast-related genes are associated with non-small-cell lung cancer survival.International journal of cancer · 2025Article
- lncRNA-AC079061.1/VIPR1 axis may suppress the development of hepatocellular carcinoma: a bioinformatics analysis and experimental validation.Journal of translational medicine · 2022Article
- Measuring differential treatment benefit across marker specific subgroups: The choice of outcome scale.Contemporary clinical trials · 2017Article
- Survival of Patients with Serous Uterine Carcinoma Undergoing Sentinel Lymph Node Mapping.Annals of surgical oncology · 2017Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
An increased interest has been expressed in finding predictive biomarkers that can guide treatment options for both mutation carriers and noncarriers. The statistical assessment of variation in treatment benefit (TB) according to the biomarker carrier status plays an important role in evaluating predictive biomarkers. For time-to-event endpoints, the hazard ratio (HR) for interaction between treatment and a biomarker from a proportional hazards regression model is commonly used as a measure of variation in TB. Although this can be easily obtained using available statistical software packages, the interpretation of HR is not straightforward. In this article, we propose different summary measures of variation in TB on the scale of survival probabilities for evaluating a predictive biomarker. The proposed summary measures can be easily interpreted as quantifying differential in TB in terms of relative risk or excess absolute risk due to treatment in carriers versus noncarriers. We illustrate the use and interpretation of the proposed measures with data from completed clinical trials. We encourage clinical practitioners to interpret variation in TB in terms of measures based on survival probabilities, particularly in terms of excess absolute risk, as opposed to HR. Clin Cancer Res; 22(9); 2114-20. ©2016 AACR.
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Registered trials
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