igor rudychev phd thesis

Massachusetts Institute for Technology. The tutorial will review the essentials of patient-flow and epidemiology forecast models. We will examine 24-monthly growth trajectory, market share and ultimate TRx and NRx volume forecasting, considering all market dynamics (trend, level, seasonality and events). 5-7 years) and the 24-month share trajectory for new products. He is a deep content expert in the commercialization of science-based assets. We will also estimate future longer-term performance based on the 1st 12-24 months of a product launch. Since epi models are traditionally annual, we will demonstrate how to reconcile the annual patient model and the monthly TRx/unit model.

igor rudychev phd thesis

D., President and Founder of The Marketing Advantage, Inc., and Richard DiPalma, Sales Manager, The Marketing Advantage.
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The marketing science training courses authored and delivered by Jerry are the gold standard of the industry and have been attended by over 10,000 professionals. Additionally, has been a leading figure in life sciences consulting and his work has been used to analyze and support products comprising over 50 of the world's pharmaceutical market value. We provide examples and a "how-to" demonstration of building an Excel model that automatically adapts market shares as product "launch dates" are moved (typically quarterly estimating both peak share (e.g. It is always best to triangulate the results of both epi and demand models. In Marketing Science from McGill University, has been a luminary in the biopharmaceutical industry for over 25 years. His work has supported each of the top 50 pharma / biotech firms in the world supporting new and inline products comprising more than 50 of the current global pharmaceutical sales value. We will also look at the triangulation of the results of a monthly demand model and annual epi-based models. Foster is a decision scientist and biopharmaceutical strategist.

The objective of this tutorial is to understand monthly demand-based quantitative time series forecasting by taking a deep dive into the short and mid-term range of a new product forecast. Many markets do not have demand data; there may be no products (orphan disease or the data is poorly tracked (low volume or direct distribution markets). Therefore an understanding of traditional epidemiological models is essential. Other topics that we will include are "new to brand dynamics" and s-shape versus negative exponential launch curve trajectories.

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