Machine Learning Frameworks for Predicting Heterogenous Treatment Effects in Clinical Trials | Kisaco Research
  • Predictive approaches to treatment effect heterogeneity (PATH), and offer the potential to identify predictive biomarkers, and to understand which treatment a patient may be more likely to benefit from
  • The two primary classes of PATH models include risk models and more flexible effect models, with a proliferation of newly published approaches in recent years
  • Limitations of these approaches such as how to appropriately evaluate their accuracy are an area of active research
Speaker(s): 

Author:

David Paulucci

Director of Data Science
Bristol-Myers Squibb

David Paulucci

Director of Data Science
Bristol-Myers Squibb