Cancer Drugs Survival Analysis to Support Affordability of Immunotherapy Treatments

Challenge Completed!

The team explored various models available in the survival analysis literature and identified the best-performing algorithms. The model predicts the survival probability of a patient and the next treatment period for specific, often less costly, drugs.

The project partner Mango Sciences is a Boston-based leading emerging market data science company connecting millions of underrepresented patients to precision medicine. The company’s Querent™ platform utilizes industry-leading AI analytics to transform deep clinical data into key insights that drive global health improvements.

The problem

The vast majority of patients who unfortunately get diagnosed with cancer, can’t afford the life-extending cancer drugs targeted immunotherapies due to high price tags. As a result, most patients use older chemotherapy medications which have significant side effects and poor outcomes.

Survival Analysis is a branch of statistics developed initially to analyze the expected duration of lifespans of individuals. It is also known as duration analysis, time-to-event analysis, reliability analysis, and event history analysis. In the case of cancer treatments, it can be used to predict the survival probability of a patient or the next treatment period for specific drugs.

The project outcomes

Mango Sciences has developed a financing product for immunotherapies that helps patients’ families pay for their drugs over a period of time, but they only pay the full value of the drug if they get the clinical benefit. Mango Sciences is building predictive algorithms to identify which type of drug works best in which patients based on their specific characteristics. Fundamentally, the right drug should go to the right patient at the right time. Patients and their families should only pay the full value for drugs if they receive a clinical benefit that is financed over a period of time.

The team explored the standard set of models available in the survival analysis literature and identified the best-performing model with the highest concordance index. An example visualization and prediction in Tableau can be found below.