Patient-based forecast models follow patient cohorts through a framework which is the forecasters best understanding of a market’s dynamics at a given time. Each forecast tells a story through which we can see all the anticipated events and their impact over a specific time period. The longer this period, the greater the risk that unexpected events will impact the model.
The primary driver of a patient-based model is usually the treated population being fed into the model. This population interacts with the dynamics set up by the forecaster to produce outputs which are hopefully indicative of what is happening in the market. If the treatment pathway for patients with a particular disease is well understood, this is usually a relatively easy population to derive.
However, issues may arise when there is a substantial shift in the treatment algorithm driven by new techniques or therapies occurring upstream of your market of interest. This may lead to substantial unaccounted for shifts in patient flow which your model is now unable to replicate. One example of this could be the approval of sunitinib as an adjuvant therapy for Renal Cell Carcinoma (RCC). Prior to this event, resection was the only possible treatment for locally advanced or high risk RCC. This approval could significantly improve patient outcomes and reduce the number of patients who enter the first-line metastatic treatment setting, which in turn could drive different outputs for subsequent treatment populations.
Any downstream market forecasts need to be aware of upstream events if they are to provide a good picture of true market dynamics over time, therefore a forecaster should always be looking for potential changes to a treatment algorithm and how this could impact their model.