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    Kristian Barker

    So you have a scenario for your forecast where you are trying to model not only an acute treatment, but a patient population that once treated, they are immediately removed from the patient pool. A good example of this is that of a vaccination.

    In a standard epidemiology forecast, as this is cross sectional by time period, the usual starting point is that of prevalence. This is because at each time period, you need to consider all the patients with the specific condition. However, taking the example of vaccinations, then a more useful starting point could be that of incidence. As these patients are effectively removed from the patient pool as soon as they have received their vaccination, a prevalence based model would potentially overstate the patient opportunity. By taking an incidence approach, this risk is negated and more accurately reflects the market that you are forecasting.

    And of course, when it comes to converting your patients to revenues, you would simply allocate a single unit to each patient (assuming it was a one-time injection for the vaccination) and a compliance rate of 100%. That way, the patients you are forecasting receiving your product will be equal to the number of units. This then becomes a ‘price per patient’ forecast as opposed to a ‘price per unit’ forecast.

    Feel free to download an example model that demonstrates the above principles.

    • This topic was modified 1 year, 2 months ago by Admin bar avatarKristian Barker.

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