Modeling Treatment Switching in Clinical Trials using Machine Learning with Dynamic Clinical Inputs

Thomas M. Menino Convention & Exhibition Center 

Traditionally, clinical trial designs treat patients switching from one treatment arm to another as a random process. However, evidence from completed studies shows that crossover occurs in a clinically driven manner and is related to clinical factors, such as disease stage, ECOG performance status and adverse events. The decision to switch treatment arms may also be driven by ethical and safety reasons. We propose using machine learning models to study patterns in treatment switching and build predictive models for treatment switching based on individualized, time varying information such as baseline characteristics, adverse events and changes in disease progression reported throughout follow up. Our proposed framework provides a data driven approach to quantify non random treatment switching patterns. This has potential implications for improved study design, sensitivity analyses, and interpretation of treatment effects in the presence of post randomization treatment switching.

treatment switching

clinical trials

machine learning 

Biopharmaceutical Section