Dry and Wet Age-Related Macular Degeneration | Treatment Algorithms: Claims Data Analysis | Wet Age-Related Macular Degeneration | US | 2018
The wet age-related macular degeneration (AMD) therapy market in the United States is unique given the dominance of Genentech’s Avastin, an oncology drug that is used off-label to treat wet AMD, despite the availability of two highly efficacious approved therapies—Genentech’s Lucentis and Regeneron’s Eylea. These current therapies, which target vascular endothelial growth factor (VEGF) and have been shown to maintain and even improve visual acuity in wet AMD patients, compete directly for patient share. It is essential to understand the use of these agents in the nuanced wet AMD treatment algorithm ahead of anticipated launches of emerging products.
QUESTIONS ANSWERED
What patient share do key therapies and brands garner by line of therapy in newly diagnosed wet AMD patients? What are the quarterly trends in prescribing among recently treated and newly diagnosed wet AMD patients?
How do wet AMD patients progress through lines of therapy, and how long do they remain on treatment before progressing?
What proportion of wet AMD patients receive drug therapy within 365 days of diagnosis, and how quickly? What percentage of patients progress to later lines of therapy within 365 days of diagnosis?
Are most recently treated wet AMD patients with each key brand coming through new (adds/switches) or continuing business?
What are the product-level compliance and persistency rates among drug-treated patients with wet AMD? How do physicians’ decisions regarding brand dosing frequency impact traditional measures of persistence and compliance?
PRODUCT DESCRIPTION
Treatment Algorithms: Claims Data Analysis provides detailed analysis of brand use across different lines of therapy using real-world patient-level claims data so that clients can accurately assess their source of business and quantify areas of opportunity for increasing their brand share.
Markets covered: United States
Real-world data: Longitudinal patient-level claims data analysis