Presentation: Forecast Treatment across Multiple Therapy Lines

Forecasting the future is always difficult, however some diseases are particularly challenging.

Oncology, respiratory diseases and diabetes all have diverse patient populations who receive multiple lines of therapy involving many different combinations of drugs and drug classes. The difficulty of the forecasting task is magnified further with the selection of subsequent lines of therapy often depending on how the patient responded to previous therapies. Information that fails to consider these complexities will not only be inadequate, but worse may be very misleading.

This presentation, delivered at the 2015 PBIRG Biotech Education Workshop Series describes a new approach to forecast treatment across multiple therapy lines. The method simulates the treatment decision process by presenting physicians with a diverse set of realistic multiple myeloma patient profiles, having them treat the patients, and receiving feedback after each treatment on its effectiveness. Treatment decisions are assessed over three lines of therapy and include decisions for both induction and maintenance therapy. Physicians make their treatment decisions under two scenarios – the market as it currently exists and a future market after the launch of an innovative new treatment.

Key Takeaways

  • Learn about the benefits of simulation over allocation in forecasting
  • Understand how to incorporate patient and treatment complexity into forecasting models
  • Gain perspective on how to incorporate future products/indications into these complex models

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