Building an assumption matrix is critical for the successful launch of a new product. It serves as a key indicator for predicting future market performance. It helps address questions such as the expected peak market share for the product and the time required to achieve it. Identifying and aligning with the right “analogue” is particularly complex, where the product evolution curve plays a vital role in estimating or modeling the uptake of new product launches.

The process of screening and selecting an analogue involves evaluating various indicators such as therapy area alignment, primary and secondary care settings, critical care environments, treatment settings or therapy lines, unmet medical needs, clinical data evidence, real-world evidence (RWE), adverse event profiles, dosing regimens, and sales performance in terms of value and volume

Although directly applying an analogue has limitations, product launch data can be mined and analyzed to identify consistent trends and advanced predictive factors that help estimate a new product’s potential performance. The right set of analogues can be identified by utilizing the sample distribution theorem and considering multiple dependent and independent variables, ultimately establishing a benchmark product as the most suitable analogue.

Certainly, identifying and applying the right set of analogues can be challenging. However, various data points, such as country-specific healthcare utilization data, market access data, disease burden data, and sales data, can be collected and utilized for modeling purposes.