Combining Secondary & APLD Advanced Analytics and Primary Analytics

What do you do if your primary research and secondary analytics don't come up with the same conclusions? At a recent PMSA conference, Igor Rudychev, head of U.S. digital, data, analytics and innovations at AstraZeneca Oncology gave a presentation that delved into this issue.

Here's the crux of it.

Historically, we know that primary market research drives pharmaceutical decision-making. Senior leadership is making major strategic and tactical marketing decisions based on a variety of factors, including:
  • Awareness and familiarity with the drug
  • Percentage of trialists, prescribers or switchers
  • Perceptions (including perceptions of efficacy and tolerability)
  • Likelihood to prescribe in the future
  • Discussions with sales reps
  • Barriers to prescribing
  • Brand perception and satisfaction
  • Influencer nominations/mapping
  • Inputs to forecasts
  • Market shares
When you get into secondary analytics, we know that the data is coming primarily from the patient level.
  • Optimization
  • Targeting
  • Segmentation
  • Prescriber analysis, including early or late adopters and historical prescribing patterns
  • Sources of business
  • Durations of therapy
  • Spheres of influence
  • Inputs to forecasts
  • Market shares
But, here's the problem. In pharma, decisions are made by using primary data, but that data is incomplete. Sales decisions are made by using the secondary data. A combination of the two is the optimal way to improve patient outcomes. But the results of those two methods, even when measuring the same thing, come up different.

Two Models Research the Same Thing, Different Results. Now What?

A way to look at this is to look at the goals of primary research and secondary analytics. Many of those goals overlap to answer the same questions.

Primary goals:
  • Market shares
  • Inputs to forecast
  • Influencer nominations/mapping
  • Likelihood to prescribe in the future
  • Had discussion with sales rep
  • Sources of business
  • Durations of therapy
Secondary goals:
  • Market shares
  • Inputs to forecast
  • Influence mapping
  • Innovators/Laggards Analysis, probability to prescribe
  • Call execution
  • Sources of business
  • Durations of therapy
Some of those goals overlap, but the research of the two methods can come up with different results. Say that, in primary research, you find a market share of 30% and a likelihood to prescribe of 80% and had a discussion with a sales rep comes in at 40%. Great! But your secondary analytics find that market share is at 41%, probability to prescribe in the future based on analogs is 50% and sales rep discussion is at 70%.

It happens much of the time because of a difference in assumptions in the two methods. So, now what?

Triangulation!

The key is to triangulate the data and look at the subset of where the triangles meet.

Let's use one example. Using a machine learning/AI model, you can create a subset from, say, claims that imitate complete HCP and patient populations and that are representative of the payor and patient population. You can then train the ML/AI model on this subset and estimate market shares, making sure to capture the parameters driving initial data skews.

Then, you can apply the model to the primary research subset and compare the numbers. This improves the model.

It's further possible to create behavioral HCP segmentation based on both primary and secondary data using that overlap and use the results for targeting.

The point is to link primary and secondary data to train the ML/AI model. It's about linking attitudinal primary variables with secondary variables in claims.

Pros and Cons

When you're talking about the projection of attitudinal variables for every HCP for targeting, the standard approach is to just use secondary variables from claims to create secondary segments.

What if, with the Qual Variables Projection approach, you project variables from primary research to the secondary data HCP? Here are the pros and cons.

Standard approach
  • Uses only secondary data for individual HCP parametrization
  • Uses only secondary data for targeting
  • Often secondary data is not enough to create uniform segments
Qual Variables Projection approach
  • Allows to probabilistically introduce qual variables to the secondary data
  • Models data first and improves projectability of segmentation
  • Creates more uniform segment
So, what do we learn from all of this?

AI and ML allows us to bridge primary research and secondary analytics. It also allows us to resolve major differences between results of primary and secondary data analysis. Many primary research techniques could be improved with secondary data analytics.

Bottom line: It's important to communicate to the leadership and decision-makers that pure primary research data could be skewed. Data enhanced with secondary analytics should be used in their strategic and tactical decision-making instead.

Complete data allows us to understand which medicines work best for which patients. In the end, it's about saving lives.


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