American Association for Cancer Research
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Supplementary Figure S2 from Quantifying Treatment Benefit in Molecular Subgroups to Assess a Predictive Biomarker

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journal contribution
posted on 2023-03-31, 18:29 authored by Alexia Iasonos, Paul B. Chapman, Jaya M. Satagopan

Kaplan-Meier curves for progression-free survival in the intention-to-treat population (Panel A); among patients with tumors that were positive for the programmed death 1 ligand (PD-L1) (Panel B) and among patients with PD-L1-negative tumors (Panel C)



John K. Figge Research Fund



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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