Like the pharmaceutical industry as a whole, the market research and business intelligence functions within have experienced a tremendous transformation over the years—and continue to change. With increasing pressure on resources, heightened competition, and growing complexity of the market landscape, market researchers and the teams the support often need to do more with less, deliver faster, and—importantly—keep up with the rapidly evolving research methodologies and data sources. This has necessitated a continuous adaptation and even greater need for translation and synthesis of data and analyses from a growing list of sources—a tall order indeed!
For decades, market research professionals have relied heavily on quantitative physician surveys and chart audits for uncovering treatment patterns and prescribing trends across patient segments, in order to inform commercialization decisions. Primary market research—both quantitative and qualitative—remains an irreplaceable tool for understanding drivers of disease management and patient treatment. However, increasing access to real-world data (RWD), including claims and electronic health records, has exponentially increased robustness of patient-level data analysis across pharmaceutical markets and enabled a more accurate foundation for market analysis.
A major caveat is that working with RWD can be costly and time-consuming, and requires an army of therapeutic and analytical experts to produce quality insights. The good news is, pharmaceutical market insights professionals now have access to robust RWD-powered solutions that make analysis accessible, quickly digestible, and reliable, making it easier than ever to integrate into decision making processes.
We can now build an accurate picture of the clinical journey using RWD.
Treatment Algorithms: Claims Data Analysis, a proven solution from DRG, analyzes deep longitudinal US claims data to understand the therapeutic landscape and prescribing trends within a disease, creating a detailed roadmap of the treatment journey. We explore how patients advance through different lines of treatment following diagnosis, which drugs they start on and which ones they progress to, and how many patients utilize various types of therapies—and for how long.
Key findings from Treatment Algorithms: Claims Data Analysis include:
- In Chronic Heart Failure: A higher percentage of newly-diagnosed HFrEF patients progress to treatment within one year of diagnosis, compared with HFpEF patients (61% vs. 38%). HFrEF patients also progress to treatment faster (32 days on average), and are more likely to progress to subsequent lines of therapy than HFpEF patients.
- In Migraine Prophylaxis: The top two drug classes used for migraine prophylaxis are antiepileptic drugs (53% share) and beta blockers (32% share). Recently-launched anti-CGRP MAbs continuing to grow share, driven primarily by first-to-market Aimovig, followed by Ajovy and Emgality. Interestingly, nearly half of patients on Aimovig were prescribed the drug as part of a combination regimen, with the goal of bossing therapeutic gains.
- In Alopecia Areata: Treatment providers are comfortable with the local application of high-potency steroids; nearly all drug-treated newly diagnosed patients receive injectable corticosteroids or topical corticosteroids (TCSs) as first-line therapy, with half of the patients initiated on injectable triamcinolone acetonide.
Understand true prescribing patterns using longitudinal claims data, and identify opportunities to increase your brand performance with DRG’s Treatment Algorithms:
- Examine physicians’ actual prescribing behavior to quantify the path from diagnosis and the treatment journey.
- Understand brand shares and quarterly dynamics among newly diagnosed patients.
- Quantify the opportunity to position your product in earlier lines of treatment.
- Capture data from recently treated patients and define the source of business for both your brand and your competitors.
- Identify which competitors are a threat to your brand and which represent an opportunity for you to capture market share.
- Understand drug usage trends (persistency and compliance) for more accurate forecast modeling.
- Update your models using detailed patient flow diagrams that follow newly diagnosed patients from treatment initiation to switching.
The data source used for all queries analyzed in the Treatment Algorithms: Claims Data Analysis studies is Truven Health Analytics’ MarketScan database, specifically the Commercial Claims and Encounters and Medicare Supplemental products.