Application of  R  programming in Clinical Trial Data Analysis

In recent years, Data Sciences has been powering vital business decisions made by industry leaders. Data scientists tell a story. They frequently need to dive through data, clean, transform, develop, verify models, analyze trends, produce insights, and, most importantly, effectively convey outcomes.

• Along with SAS, the most discussed languages in Statistics, Analytics, and Visualization are R and Python. This thing discusses the present state of R, the problems that have been noticed, and the recommended techniques for risk assessment of R packages, mitigation, and implementation for Clinical Trial Data Analysis.
• The industry is looking for better alternative technologies and more sustainable technologies and may give ideal solutions to industry issues. Are our existing tools out of date? Do we have an alternative SAS solution (for example, Big Data, Real World Data) to process it better and more accurately? Efficiency in Data Analysis leads to deeper insights into data and can assist in decision-making across the Drug Development Process.
• Innovation is required to move away from any conventional inefficient processes/tools and toward efficient, simple, easy-to-implement, dependable, and cost-effective alternatives. Collaboration among industry stakeholders is required to build better technological ecosystems and reach a consensus on Validation and Regulatory standards.

Current Trends of R in Pharmaceuticals: According to recent industry trends, R utilization in activities connected to Pharmaceutical Regulatory Submissions is less than 10% at present. On the other hand, R is widely used in public health initiatives, healthcare economics, exploratory/scientific research, trend detection, plot/graph production, specialized statistical analysis, and machine learning. R is not frequently used for creating CDISC (SDTM, ADaM) datasets. We have a few R early adopters that have encountered some difficulties. One of the most prevalent difficulties in ensuring the regulatory compliance of R packages. Suppose R is used in regulatory filings, risk assessment of R packages, feasibility analysis, and establishment of a process for R usage through Pilot Projects with the necessary documentation.

References

Chen, Ding-Geng Din, and Karl E. Peace. Clinical trial data analysis using R. CRC Press, 2010.