03. A Collaborative Workflow for Efficient Exploratory Biomarker Planning: Reducing Costs and Patient Burden via Statistical Optimization Tools

Exploratory biomarkers are crucial in clinical trials as they help identify potential therapeutic targets, understand disease mechanisms, and predict patient responses. However, their collection and analysis can be expensive and can impose a significant burden on patients, requiring sometimes frequent and invasive sampling procedures. Here, we introduce a biomarker optimization workflow that is aimed at enhancing the efficiency and effectiveness of exploratory biomarker planning by incorporating statistical power analyses and cost optimization models. The workflow leverages both analytical and simulation-based power calculations to identify opportunities for sampling a reduced number of subjects or reduced timepoints for each exploratory biomarker, while maintaining statistical power. An additional innovation is a user-friendly RShiny app for implementing the optimization workflow, facilitating real-time collaboration between clinical and statistical teams. Unlike general power analysis packages that offer a wide range of methods, this app focuses solely on the statistical tests commonly performed on exploratory biomarkers, including but not limited to association, pharmacodynamic, and survival analyses. This ensures a smooth user experience and maintains consistent assumptions across trials. In addition to informing biomarker collection based on statistical power, our methods can incorporate surrogate based optimization to account for complex cost functions, to account for dynamic pricing, site specific overhead and other pricing factors. In simulated late-stage oncology clinical trial plans, we demonstrate that this workflow can reduce costs by a large margin (mean = 15%, SD= 7%), while maintaining statistical merit (power= 0.8) across diverse assay types. Overall, we demonstrate a collaborative and data-driven approach to biomarker planning that enhances the efficiency of clinical trials, while preserving statistical integrity of exploratory biomarkers.

Biomarkers

Optimization

Power Analysis

Clinical Trial Design 

Expert

Knowledge

Women in Statistics and Data Science 2025