An integrated methodological roadmap for real-world biomarker studies: advancing oncology precision medicine through...

Area of Science:

• Oncology
• Biomarker Research
• Real-World Evidence

Background:

• Biomarker-specific patient selection is crucial for targeted cancer therapies.
• Successful implementation requires awareness, adoption, and accessibility of biomarker testing.
• Real-world studies (RWS) are vital but face challenges like bias and missing data.

Purpose of the Study:

• To provide an integrated methodological roadmap for designing and analyzing RWS in biomarker research.
• To address challenges in RWS, including confounding factors, biases, and data complexities.
• To enhance the validity and robustness of real-world biomarker research for clinical decision-making.

Main Methods:

• Integration of robust statistical methodologies with advanced machine learning (ML) methods.
• Utilized time-dependent Cox models to mitigate immortal time bias.
• Employed inverse probability of biomarker weighting for confounding adjustment and ML-based tree ensembles for complex relationships and missing data.

Main Results:

• The proposed roadmap enhances the validity and robustness of real-world biomarker research.
• The integrated approach overcomes common analytical pitfalls in RWS.
• Enables more reliable evidence generation for clinical decision-making in precision oncology.

Conclusions:

• This methodological roadmap provides a systematic approach to conducting high-quality real-world biomarker studies.
• It facilitates the generation of sound evidence to support biomarker-driven therapeutic strategies.
• Drives precision oncology forward by ensuring reliable real-world evidence for clinical application.