This paper is like a buyer’s guide for how to analyze whether a new drug works in clinical trials, comparing traditional statistics with newer AI and machine‑learning methods.
Choosing and validating the right analytical methods to assess treatment efficacy in clinical trials, balancing regulatory acceptance, statistical rigor, and potential gains from newer ML techniques.
Regulatory know‑how, domain expertise in trial design, and access to large historical clinical trial datasets that can be used to benchmark and validate methods.
Classical-ML (Scikit/XGBoost)
Structured SQL
High (Custom Models/Infra)
Data access, integration, and strict privacy/compliance constraints for patient‑level clinical trial data; plus the need for extensive validation for regulatory acceptance.
Early Majority
Focuses specifically on efficacy analysis in clinical trials, framing both classical biostatistics and machine-learning approaches in a single comprehensive review tailored to pharma/biotech R&D and regulatory contexts rather than generic healthcare ML.
3 use cases in this application