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Dr André Brunella, University Hospital Basel, DKF Basel
The magnitude of data being processed in the clinical context is ever increasing, and thus automated routines and algorithms are needed that allow efficient processing and analysis. Since the Clinical Trial Units (CTUs) were established at Swiss hospitals in the late 2000s, each CTU has developed such methods individually. The SCTO’s platforms provide the opportunity to unify these methods by combining expertise from the individual CTUs.
Members of the Statistics and Methodology Platform and the Data Management Platform joined forces to develop a solution that was implemented in three steps. First, data processing solutions were consolidated within the R statistics environment throughout the entire CTU Network. Then an R package was developed to handle data from the clinical data management system (CDMS) secuTrial®. In a third step, this package was made available to the public.
R is a programming language specifically designed for statistics, and it has become increasingly important since first appearing in the mid 90s. Its large open source community and easy-to-use structure make it an ideal tool for data managers and statisticians in the CTU Network.
The now publicly available R statistics package simplifies working with secuTrial® data exports. It facilitates the loading of data and the conversion of variables to formats more suited to analyses. Furthermore, functionalities for descriptive analyses (e.g. study recruitment, data completeness) are included.
The released R statistics package secuTrialR is available on the Comprehensive R Archive Network (CRAN), GitHub, and Anaconda Cloud. In addition, a paper on the development of secuTrialR was published in The Journal of Open Source Software.
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Unprecedented advances in recent decades have enabled researchers to collect an abundance of scientific and clinical data. Research emerging from this data can only be excellent when the underlying data handling practices are of the highest standards, too. Data practices must be professional, transparently documented, and – most importantly – reproducible.
In the clinical research setting, data management professionals supply the infrastructure and expertise needed to run affordable and high-quality databases. These contributions ensure that data collection can comply with regulations and be efficiently retrieved and processed, so it is in line with the original research goals.
Study data collection must adhere to Good Clinical Practice, the Swiss Human Research Act, legal and ethical considerations, and international regulations. With the support of their data management peers, researchers can establish databases which serve their research needs, that stand up to scrutiny and allow regular reporting, data cleaning, and automatic validation.
