Comparing algorithms for characterizing treatment effect heterogeneity in randomized trials.

Area of Science:

• Biostatistics
• Clinical Trial Design
• Pharmacometrics

Background:

• Assessing overall treatment effects is standard in clinical trials, but identifying subgroup-specific effects is vital for personalized medicine and drug development.
• Estimating heterogeneous treatment effects (HTE) is challenging due to trial designs focused on average effects.
• Understanding HTE is critical for optimizing therapeutic strategies and advancing precision medicine.

Approach:

• This work reviews existing simulation studies on HTE identification and estimation.
• A novel simulation study is conducted to evaluate recent HTE methods using metrics and scenarios relevant to drug development.
• The R package 'benchtm' is introduced for simulating biomarker distributions and creating benchmark scenarios for HTE methods.

Key Points:

• The study benchmarks methods for identifying and estimating HTE in simulated clinical trial scenarios.
• Performance evaluation focuses on scenarios reflecting real-world clinical trial data and complexities.
• The 'benchtm' package facilitates reproducible benchmarking of HTE estimation techniques.

Conclusions:

• Accurate identification and estimation of HTE are essential for effective drug development and personalized treatment strategies.
• The simulation study provides insights into the performance of different HTE methods under realistic conditions.
• The 'benchtm' package offers a valuable tool for researchers to assess and compare HTE methods in drug development.