Designing phase II clinical trials to target subgroup of interest in a heterogeneous population: A case study using...

Area of Science:

• Clinical trial design
• Biostatistics
• Oncology research

Background:

• Classical Phase II trial designs struggle with molecular heterogeneity in biomarker-targeted therapies.
• This can lead to erroneous conclusions about treatment efficacy in the overall population.
• Subgroups of patients may benefit, but current designs may fail to identify them, impacting Phase III trial targeting.

Purpose of the Study:

• To introduce and evaluate stratified adaptive two-stage designs for Phase II trials with biomarker-selected patient populations.
• To address the limitations of classical designs in identifying patient subgroups that benefit from targeted therapies.
• To present an R package (ph2hetero) for implementing these advanced trial designs.

Main Methods:

• Review of stratified adaptive two-stage designs proposed by Jones, Parashar, and Tournoux et al.
• Development and presentation of the R package 'ph2hetero' for implementing these designs.
• Illustration of designs and package utility through a case study.

Main Results:

• Stratified adaptive designs allow for the identification of patient subgroups (biomarker-positive/negative) within a single study.
• These designs can accurately assess anti-tumor activity and clinical utility of biomarkers.
• The 'ph2hetero' R package provides a practical tool for applying these methods in oncology research.

Conclusions:

• Stratified adaptive designs offer a valuable alternative to classical two-stage designs for Phase II biomarker studies.
• These methods enhance the ability to detect treatment benefits in specific patient subgroups.
• The presented R package facilitates the adoption of these advanced designs in clinical research beyond biomarker studies.