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Epidemiology-Based Market Forecasting: Pros and Cons
Decision Resources, Inc, March 2005, Pages: 10
Demand forecasting is a key activity for biopharmaceutical companies. Its many applications range from product-level volume forecasts that drive near-term manufacturing schedules and resource allocation to 10- to 20-year market forecasts that inform investment choices, go-no go decisions on individual projects, and new product planning. Quantitative, data-driven forecasting can be created in two ways. 'Bottom-up' forecasts (also known as epidemiology- or patient-based forecasts) build up estimates of demand from as close to the ultimate consumer as possible. Typically, they build up prescription or sales demand from consumption at the individual patient level. In contrast, 'top-down' pharmaceutical forecasts extrapolate historical demand (e.g., as dollars, prescriptions, grams of bulk drug) into the future and overlay the effects of key events. For short-term tactical forecasting (within a one- to two-year horizon), top-down methods may be most useful. For product and market forecasts with a medium- to long-term (i.e., 5- to 20-year) horizon, epidemiology-based forecasting is more appropriate.
In this report, we discuss the advantages and disadvantages of using the epidemiology-based method of forecasting.
Business Implications
- Epidemiology-based forecasting is a transparent and rigorous approach to projecting the sales implications of critical events and overall market evolution. Central to this methodology is the ability to disaggregate the effect of events into the impact on each component of the patient-based forecast cascade. Instead of basing the sales implications of a key event on some informal consolidation of all the effects on all the multiple factors driving demand, an analyst can specifically examine and project the effects of the event on one driver at a time.
- Patient-level data, if available, can improve the accuracy of base-year diagnosis rates, overall drug-treatment rates, compliance rates, and drug-treated days per time period. In the absence of suitable patient-level data (or relevant studies in the medical literature), estimates from physicians are commonly used. However, such estimates are often of questionable accuracy. The uncertainty surrounding many of the inputs of a patient-based model highlights the need to triangulate base-year sales (when available) with top-down sales estimates.
- Even in indications or market segments for which accurate patient numbers appear to exist, designers of epidemiology-based forecasts face a major challenge in matching the epidemiologist’s strict definitions of a disease to the less formal criteria of the prescribing physician. As a result, the epidemiologist’s estimate of patient numbers will sometimes be low compared with those of the physician, creating a “disease definition gap.” This discrepancy can lead to unwarranted alterations in key forecast inputs or systematic underestimation of market size.
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