Tethering to Reality: Integrating HCP-level Real-World Data into Brand Share Estimation

Share estimates from demand studies in primary market research (PMR) are a critical input for any forecast—their accuracy is paramount for successful brand planning.

Successful prediction is often elusive—from 2020 through 2022, only one in ten products had actual performance that aligned with forecasted expectations.*

Recognizing this challenge, Trinity experts developed a method that significantly improves prediction of future prescribing behavior in demand studies.

The Challenge

It is often difficult for Healthcare Professionals (HCPs) to offer precise share estimates when responding to surveys in PMR. HCPs typically do not think about their prescribing behavior holistically in their daily practice—they may have recall bias, recency bias towards newer brands, or some combination of factors. Some of these estimation errors may be overcome by using real or simulated patient chart-based methods (e.g., patient simulation, chart abstraction) that capture clinical decision-making rather than methods based on an aggregate recall of their prescribing behavior (i.e., percentage-based share estimates from a share allocation exercise). Still, there are several factors that physicians may not be able to appropriately consider when thinking about a future market being evaluated in the study, such as patients actually filling or refilling the script, insurance coverage, formulary restrictions, sales and marketing efforts by the manufacturer, etc.

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