Navitas Life Sciences is committed to improving clinical trial efficiency to lower cost and time to market, by investing in the latest digital tools. Leveraging such technology aids in trial management with near real-time analytics that aid in better decision making for efficient clinical trial management.
Our expert Shrishaila Patil, Vice President, Statistical Programming, Navitas Data Sciences has been championing the use of R programming in clinical trials, with an article on the same published in BioSpectrum India Read the full article.
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Navitas Life Sciences has become increasingly reliant on data analytics to ensure efficient virtual clinical trial management. The use of such measures, even before the pandemic, aided in smooth transitions to a fully virtual trial during pandemic mediated restrictions.
Virtual clinical trials generate a large amount of data, which can be used for better patient engagement, and to lower risk in clinical trial conduct. Analyzing such data is an important step that is often dependent on specialized software or commercial spreadsheets. Such methods may not be appropriate during clinical trials with increased complexities or may be limited in functions. Moreover, such programs are known to be associated with lower reproducibility as well as transparency.
R Programming
R programming is freely available comprehensive, platform-independent programming language that is ideally suited for managing clinical trial data. It was developed by Ross Ihaka and Robert Gentleman in the 1990s for data handling, effective cleaning of the data, subsequent analysis, and a good representation of the results.
Here are 8 exciting reasons to use R programming in clinical trial data analysis:
1. R is Open Source: The R software is open source, which means that this freely available programming language can be used by downloading at no cost.
Benefits of using an open-source software
- No cost incurred
- Underlying code can be edited, if required
- There is no commercial element to fixing bugs
- Ongoing development by a large number of developers
2. Large user community: R is popularly used by data driven-industries, as it is possible to adapt the code developed on one platform onto another. Most statistical methods are likely to have at least one add-on package that is available free to be implemented within the R environment.
3. Integrated tool for sharing results: There are a number of tools available in R that let you share and communicate the results. This paves the way for a dynamic reporting system, with the potential for interactivity.
There are user interfaces, a web-based dashboard, and multiple applications that allow the development of interactive business intelligence-style dashboards that can be used to manage operations. The reports that are shared by R can be used by people who may not know R or have the program installed on their computer.
Benefits of Using R Programming to Communicate Results
- Advanced web-based dashboards and user interfaces
- Reports can be viewed even if the program is not installed
- Prior knowledge of R is not required to use the reports
- Analysis can be converted easily into high-quality documents and presentations
- Analysis can be conducted automatically at predefined times
4. Specially developed packages: R excels at converting data from multiple formats into standard data frames. This helps process clinical data, from preparing reports to distributing results. The results can be shared via interactive dashboards, PDF reports, or through other formats. It is also used in high-dimensional data analyses, because of the extensive package of statistical methods. There are exclusive packages for genomics and metabolomics.
| Some Packages that will aid in Clinical Trial Design and Analysis | |
|---|---|
| atable | To create tables for clinical trial reporting |
| CRTSize | Estimation of sample size |
| DoseFinding | For dose-finding experiments |
| Pact | For predictive analysis in clinical trials |
| Greport | Graphical reporting |
| BiocManager | Bioconductor package |
| MetaboAnalystR | For analysis of metabolomics data |
| Coronavirus | Daily summary of COVID-19 |
5. Efficient Scalability: R can be scaled so it can be used across the organization, beginning from one person downloading it at a workstation to collaborating on a big project. It integrates seamlessly with data science technologies that are currently popularly used like Microsoft PowerBI, Python, or GitHub.
6. Multiplatform: R can be used across platforms, from LINUX to UNIX to Windows, with multiple platform and machine learning capacities.
7. Great Graphics: R can be used to develop static graphics, or its extended libraries can be used to produce interactive graphics. Such features aid in better data visualization, with enhanced representation of data, ranging from concise charts to interactive flow diagrams.
8. Machine Learning and Big Data: R programming supports predictive analytics and big data, with the potential to leverage artificial intelligence and machine learning.
R programming is used by over 2 million users worldwide, making it one of the most popular. The essence of effective analysis is in leveraging the advantages of the various available methods, to provide key insights and analysis. At Navitas Life Sciences, we provide transformative data management support to our clients, enabled by future ready technology and sustained by our vast capabilities and experience.
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