“Later-line” products are those products that are launched with a label that requires that other treatments be tried before they are prescribed. Forecasting them is a challenge.

Key Principles

  1. Forecasting of later-line products is a complex process that involves primary market research combined with “conventional” market forecasting.
  2. Primary market research is most frequently used to determine the proportion of patients for whom the physicians will use or consider using the new drug for.
  3. Primary market research is an excellent tool for quantifying likely usage, but a very poor tool for quantifying the proportion of patients that require a later-line choice
  4. To arrive at more reliable (and accurate) segment quantification a different technique is required – involving secondary data that must integrate with the primary research to build a good forecast.

Segmentation using claims and/or EHR data

We now have access to huge data sets that inform most of the details we could need to define the segments of our market. Using claims and/or EHR data we can now define segments using multiple layers of co-morbidities, co-prescribing, historical treatments, specific test values – as well as all the usual geo-demographic characteristics.

EHR and claims datasets are very large; as a result we are able to define cohorts without concern for sampling issues that arise with chart audit data. This allows us to explore more cohorts and determine which are particularly relevant to the later line product being launched.
The cohorts that are identified as most relevant can then be used to define the cohorts used in primary research. This apparently simple and logical step is frequently forgotten, but it drives huge benefits in the final forecast.

Patient flow analysis to quantify later-line therapies

Perhaps the hardest problem in forecasting later-line products is the need to quantify the number of patients for whom a later line therapy would be appropriate. For this we use complex patient flow analysis on the cohorts defined in the claims and/or EHR data.

Complex patient flow looks at all aspects of the patient pathway, including co-morbidities, procedural actions, and drug therapies. This provides a clearer understanding of the way physicians manage patients, which gives us a more precise measure of the later-line opportunities.

Linking secondary to primary to build the forecast

The quantification of these later-line usage segments, combined with the primary MR results enable the building of a reliable, robust patient flow model that can be used to simulate the necessary scenarios. This can also be combined with other relevant analytics such as promotional response modelling.

Galileo Cosmos™ was designed from the outset to complete these complex patient flow and segmentation analyses. The results can be dynamically combined with both primary MR and a forecasting model to create a tool that can simulate the market that the new later-line product will be entering.

The speed and flexibility inherent in Galileo Cosmos™ results in a forecast that is based on:

  • The best possible use of the full suite of available secondary data
  • Integration of high quality primary research that models product choice
  • Inclusion of additional models such as promotional response and payer access
  • Scenario driven forecasts that increase management confidence in decision making

Galileo Cosmos™ is a novel tool that has been designed to support these complex tasks. Contact us to book your demonstration and a discussion of your specific needs.

Galileo Cosmos™ – Turning Raw Data into Insights in Real Time

Simon Fitall

About Simon Fitall

Simon is a knowledge engineer with 30 years experience in market research, data analytics and business intelligence within the pharmaceutical, biotechnology and medical device industries. With multiple patents in the field of advanced medical data analysis, Simon is an expert in data analysis with more than 20 years experience in working with, analyzing and creating models with patient data.

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