In the field of pharmaceutical forecasting, various types of epidemiological models are utilized to gain insights into disease dynamics. These models play a crucial role in strategic planning and decision-making within the pharmaceutical industry. Here we explore the three main model types: epi-based pharmaceutical forecasting, opportunity-based pharmaceutical forecasting, and patient flow modelling.
Epi-based modelling.
Utilises epidemiological models to gain insights into disease dynamics. This approach, known as cross-sectional epidemiological modelling, focuses on analysing data from different time periods independently, without establishing direct correlations between them. By examining the prevalence of diseases at specific points in time, this modelling technique provides an indication of all patients on treatment during a particular period. However, it does not capture the flow of patients between different time periods or stages of the disease.
Epi-based forecasting typically starts with a larger population and applies epidemiological filters to derive the target patient market. This approach is often employed in long-term planning or in markets where sales data is unavailable, making a patient-based approach more suitable. To support the modelling process, epi-based forecasting relies on a wide range of sources and literature, including prevalence rates and diagnosis rates, which are used to inform the epidemiological filtering process.
Overall, epi-based pharmaceutical forecasting offers valuable insights for strategic planning and decision-making, particularly when sales data is limited or unavailable.
Opportunity-based modelling.
Utilises a nuanced approach to analyse treatment patterns and forecasting future demand, based on different patient cohorts.
This modelling technique divides the market into distinct groups, including new or naïve patients, switch patients, and stable patients. By considering factors such as patients starting on treatment or switching treatments, the model enables a more accurate analysis of treatment dynamics. Additionally, opportunity forecasts can also account for patients dropping out of the market.
The segmented approach allows for specific assumptions and forecasts for each patient segment, considering factors such as peak share, uptake, and dosing variations. This hybrid forecasting method combines elements of cross-sectional epidemiological forecasting and incidence-based patient flow forecasting, resulting in more precise volume predictions.
Patient flow modelling.
A valuable technique that tracks patients throughout various stages of treatment and disease progression, offering a comprehensive understanding of patient populations and treatment pathways. This modelling approach incorporates incidence data to monitor patients over time and assess their progression through different stages or lines of a disease.
Patient flow modelling is commonly employed in oncology or progressive diseases where complex treatment algorithms exist. It also enables the modelling of treatment restrictions, where patients may become ineligible for certain treatments in later lines due to prior product usage. Additionally, this forecasting technique can provide value by facilitating the analysis of the impact of license extensions or the approval of new products earlier in the treatment process.
Patient flow modelling proves particularly useful in evaluating opportunities within small markets or rare diseases, where even a small number of patients can have a significant impact on sales. Overall, patient flow modelling provides a detailed and accurate assessment of the opportunity landscape, aiding in strategic decision-making within the pharmaceutical industry.
Collectively, appropriate use of these models empowers pharmaceutical companies to make informed decisions and optimize their strategies to better serve patient needs and improve healthcare outcomes.
