Analogs are a really powerful tool for forecasting, particularly when a product is in development and it’s looking to come to market and you’re trying to understand how it might perform. Because ultimately an analog is something that happened in the past. So it roots in reality. If you are projecting or saying a new product’s going to achieve a certain performance in the market, 20% of patients or 40% of volume. To root that into a reality that has happened in the past makes it very tangible and meaningful. But ultimately, there’s no perfect analog. The perfect analog is the product that you’re working on. So a really important aspect of analogs is looking at suite or tool a group analogs on a better phrase, where you’re choose analogs, which are similar as possible to the product that you’re working on to develop. And then that allows you to look at this batch or group or collection of analogs of similar products that have come in the past and take a view.

There might be a range of performance there. And then you can understand the range which you might be working in. So it’s a really valuable tool to bring into the mix, to feed in as an assumption to a forecast as you’re going through that new product planning development stage. Analogs also can be thought of in a couple of different perspectives. You can look at analogs from a revenue or volume performance, which is often useful to validate the forecast you are developing because products when they’re in development are using a patient-based forecast. A forecast where you’re thinking, how many patients are there out there, which my product could be used, what share am I going to give out patients? And then using ultimately that to convert through to volume, revenue, number, and analogs where you can look at it from a validation perspective saying, well, this product got 500 million before, therefore I’ve got a forecast of four to four-fifty million. That seems right.

You can also think of it as a patient base where say, well, this product got 20% of patients, therefore I can think I might get 20% of patients. It becomes more of an input to feed an input into forecast rather than a kind of output validation purpose. And we often see a lot of analogs out there around the volume and revenue type thinking, validation, but actually we’re developing analogs based more on the patient basis, which then feeds into the models in a more direct manner, increasing the value of the analogs and the different kind of points in the forecast model and development and validation that it can hit. It’s not just about checking it at the end point. You can bring it in at different points and then ultimately integrate it into the model so it’s all connected. It’s very easy to just choose analog and the data pops in and making it easy. So again, the forecaster can focus on the thinking rather than doing, rather than cutting and pasting data, they think about what the data means.