Modeling drugs and diseases is undoubtedly an important step in product development, but it is only a part of the equation. Even if a product, or a diagnostic, makes it to market, will it be prescribed by physicians?
Traditional market research can generate some answers, but like many other aspects in product development, when this is combined with modeling, it becomes more powerful.
In this webinar, we will talk about the problem of “Discrete Choice” and the kinds of commercialization decisions that mathematical models of discrete choice can support
We will show how, given a finite collection of competing drugs or diagnostic products, we assign each of a group of decision-makers a probability of choosing each product. These probabilities are a function of these products' characteristics and physicians’ attitudes toward these characteristics.
We will describe the mathematical underpinnings that define these models, the methods used to estimate model parameters, and how choice probabilities can be derived from them.
We will also address the real-world practice of model construction and show where in the process of moving a drug or diagnostic from the lab into the marketplace, the discrete choice problem fits.