The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
By Gordhan Bagri and Munshi Imran Hossain with H A S Shri Kishore
Shiny (from RStudio) is one of the most popular R packages. The package allows programmers to create applications with interactive user interfaces. These applications can then be deployed for non-programmers to perform analysis. Non-programmers can, therefore, make use of the statistical capabilities of R by means of point and click. This is one of the reasons why its use has been on the rise in the last few years.
Cytel data scientists apply advanced statistical techniques including predictive modelling of biological processes and drug interactions to unlock the potential of big data.
In this blog from our Career Perspectives series, we talk to Andrea Hita, at Data Scientist at Cytel, to find out more about her career path, her current role at Cytel and her interests outside of work.
Our recent Clinical Biometrics Survey explored the views of respondents from across the statistical programming, biostatistics, and data management functions to learn their top challenges, and most important perceived industry trends and skills development. In this blog, our Ajay Sathe gives his perspectives on the key areas of personal and knowledge development that he believes statistical programmers need to focus on to keep abreast of the evolving drug development landscape.
By Ivan Navarro, Data Scientist at Cytel
R is an open-source implementation of ‘S’, the statistical programming language. With its open character and ability to extend its functionality using external packages, R allows users to create their own packages that are easily loadable into the core instance.
In essence, R-packages are extensions that contain source-code, documentation, data and examples of personal contributions and can be extremely useful for data scientists, statisticians and programmers alike who need to create custom analysis and visualizations. However, creating your first R-package can be a complex task for non-experienced users.
In this blog, I explain how to create a basic R-package which can be used as template for anyone interested in making a contribution. A previous knowledge on R programming is required, but you will not need to deal with technical aspects of the creation process because the R-Package structure is shared at the end.
As we prepare to close the door on 2017, we thought we would take a look back at the topics which have been most popular on the Cytel blog this year. It's an interesting insight on what pain points and opportunities feature highly on our global biopharma audience's radar. Read on to learn which of our 2017 blogs have received the most interest from our audience so far.
Signal management is one of the most audited pharmacovigilance processes. It also generates one of the highest findings from audits. The ability of Marketing Authorisation Holders (MAHs) to make a robust signal management system that is fully audit/inspection ready sometimes falls short of expectations. Happily, technology can be used to make the process more scientific and rigorous.
Technology in the signal management process can be divided into two categories. The first one is the front end i.e. what platform (.Net/JAVA) is being used to develop the system. The second is the back end i.e. what programs/software (R, Python, SAS) are used to process the data. In this blog, we will focus on the second category and discuss how R specifically can help improve the signal management process.