Predictive Modeling: What, Why, How

Envision a day that cancer clinicians can ask an app to advise on immuno-oncology (IO) treatment options for a patient. That day may not be far off. Big data, deep analytics, and predictive modeling methods are transforming how cancer clinicians weigh treatment options.

The next frontier in IO treatment lies in harnessing highly granular data to explore treatment options by answering questions that will increase the ability of clinicians to deliver personalized medicine. Those questions include:

1. Which options might apply to this patient, but also which do not, and why?
2. Why may some options work for this patient, but some not?
   
3. What is the relative effectiveness of each of these options for this patient, and how do they stack up against each other?
   
4. What adverse events can we expect, when, and why—for this patient?
5. Which treatment options can this patient tolerate physically and psychologically, and why?
6. How do treatments compare in balancing effectiveness and safety?

This is where predictive modeling comes in: answering questions about unique patients and their IO treatments so that these treatments can be as individualized as possible.

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