A recent article published by Cytel authors Samadhan Ghubade, Sharayu Paranjpe, Kushagra Gupta, Anil Gore and colleague Krishna Asvalayan in the journal Current Science, tackles the topic of adverse drug reactions (ADRs) – a matter of great concern in drug research. The authors focused their research on drugs which had been either banned or withdrawn due to a serious problem of ADRs and applied quantitative modeling techniques to see if a systematic pattern of safety signals could be detected within the ADR count data. In this blog, the publication’s authors share their thoughts on the goals, takeaways and next steps for the research and we also link to the full article.
Jul 18, 2018 4:40:00 AM
Jul 2, 2018 10:00:00 AM
A number of the Cytel team were in Amsterdam, 3rd- 6th June 2018 for the PSI Conference. This year’s conference was held at the magnificent Beurs Van Berlage, a venue full of history and interesting architectural features. We took the opportunity to give delegates a first look at OK GO, our new clinical trial Go/No-Go decision-making software in this magnificent setting.
In this blog, we'll summarize some of the particular highlights from the sessions that our team members attended.
Jun 7, 2018 8:39:00 AM
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.
May 2, 2018 5:07:00 AM
PSI is a global member organization dedicated to leading and promoting best practice and industry initiatives for statisticians in the biopharmaceutical industry. The PSI annual conference is going from strength to strength, attracting increasing numbers of delegates from Europe and beyond. With the 2018 conference taking place in Amsterdam in only a month’s time, we took the opportunity to sit down with Lucy Rowell, Senior Principal Statistical Scientist at Roche and the Conference Chair, to learn more about this year’s venue, themes, and new hot topics, along with insights on Lucy’s vision for the future of the PSI organization. We look forward to seeing you in The Netherlands!
Mar 8, 2018 6:05:00 AM
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.
Jan 23, 2018 10:36:00 AM
News Medical interviewed Dr. Rajat Mukherjee, Statistician, and Director of Data Science at Cytel to investigate the potential of data science in clinical development.
Jan 15, 2018 8:12:00 AM
The problem of feature selection
The explosion in the availability of big data has made complex prediction models a conspicuous reality of our times. Whether in banking, financial services and insurance, telecoms, manufacturing or healthcare, predictive models are increasingly used to derive inference from data.
Most of these models use a set of input variables, called features, to predict the output on a variable of interest. For example, the concentration of characteristic biomarkers in the blood can be used to predict the presence, absence or progress of certain diseases.
The available data can provide a large number of features, but generally, it’s preferable to use a small number of really relevant features in a model. This is because a model with more features has a greater complexity which leads to greater demand on computational resources and time to train the model. Therefore it is desirable to restrict the number of features in a predictive model. Choosing the subset of features that will result in a model with optimum performance is the problem of feature selection. This is essentially a problem of plenty.
Dec 21, 2017 6:44:00 AM
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.
Dec 12, 2017 7:33:00 AM
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.
Sep 7, 2017 7:00:24 AM
By Munshi Imran Hossain, Software Affiliate at Cytel
Biomedical signals are electrical signals collected from the body. Some of the most common ones are the electrocardiogram (ECG) and the electroencephalogram (EEG). These signals are of great value because they can be used for diagnostic purposes. Importantly, most of them can be collected using non-invasive methods. These attributes, together with the tremendous recent advances in electronic and digital processing technology, have made biomedical signal data an important source of data used in medical diagnostics.