Quantifying Treatment Benefit in Molecular Subgroups to Assess a Predictive Biomarker

Area of Science:

• Biostatistics
• Clinical Trial Design
• Translational Oncology

Background:

• Predictive biomarkers are crucial for personalized medicine, guiding treatment decisions for cancer patients.
• Assessing treatment benefit variation by biomarker status is key to validating predictive biomarkers.
• The hazard ratio (HR) is commonly used but difficult to interpret for treatment benefit variation.

Purpose of the Study:

• To propose and evaluate novel summary measures for treatment benefit variation on the survival probability scale.
• To provide easily interpretable measures for assessing predictive biomarker performance.
• To guide clinical practitioners towards more intuitive interpretations of treatment effect differences.

Main Methods:

• Development of summary measures for treatment benefit variation based on survival probabilities.
• Application of proposed measures to data from completed clinical trials.
• Comparison of proposed measures with the traditional hazard ratio (HR) for interaction.

Main Results:

• Proposed measures quantify treatment benefit variation in terms of relative risk or excess absolute risk.
• These measures offer a more straightforward interpretation than the hazard ratio.
• Illustrative examples demonstrate the practical application and interpretation of the new measures.

Conclusions:

• Survival probability-based measures provide a more interpretable assessment of predictive biomarker utility.
• Excess absolute risk is recommended as a preferred measure for clinical interpretation.
• Adoption of these measures can enhance the understanding and application of predictive biomarkers in clinical practice.