Capture of real-world data of treating melanoma patients across Europe
The collection and evaluation of real-world data is of particular importance for the further development of treatment strategies, clinical guidelines and future innovation for prevention, diagnosis and treatment of cancer.
In approval studies the application of study medication is recorded and tracked in detail. However, documentation that goes beyond the data captured within clinical trials, as follow up treatment and outcome, is scarce. Hence, data that are of high value for patients, physicians and pharmaceutical companies alike are often lost. First attempts to close the knowledge gap are provided by cancer registries collecting real-world data.
In this context, data from patients that are usually excluded from clinical trials are collected. However, in contrast to clinical studies, these are often in operation in single countries only.
To provide both an alternative to ad hoc high-resolution studies and obtain knowledge on an international level EuMelaReg has been launched is year! The EuMelaReg registry collects existing data on melanoma patients throughout Europe and integrates them in a Data Warehouse operated by the EuMelaReg consortium.
Since its foundation this year, the EuMelaReg consortium has constantly been gathering speed and seven European countries have committed themselves to the scientific goals of the consortium already.
Alcedis is proud to be part of this scientific adventure and provide it with the IT infrastructure and long-standing Big Data expertise required for meeting the challenges of this international registry. From the data quality perspective the major challenges are the following:
• How can heterogeneous data structures and formats be easily merged?
• What methods can be applied for missing data fields and adjustment of inconsistencies?
Bearing in mind that restrictions on privacy and processing of bulk data making the use of well-established means for data cleaning either not feasible or too expensive.
Two highly promising ways to achieve a sound data quality are the implementation of specific validation schemes on a national basis and the application of advanced statistical methods.
You want learn more about this pioneering project? Contact us at firstname.lastname@example.org.