The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
Cytel data scientists apply advanced statistical techniques including predictive modeling of biological processes and drug interactions to unlock the potential of big data.
In this blog we talk to Munshi Imran, who is based in Pune, India to find out more about his career path, current role at Cytel and his interests outside of work.
In this blog, we share a new infographic based on this popular blog post illustrating some of the critical interactions that need to take place between data management and statistics groups to help ensure efficiency and data quality.
Data management is an essential building block for successful Immuno-Oncology (I-O) trials. At the Immuno-Oncology Clinical Trials operations meeting in New York in earlier this year, Patti Arsenault, VP Quality Assurance at Cytel discussed with Christopher Lamplugh, AVP, Clinical Data Management, Global Data Operations at Merck, the key challenges for data management in the space, and what’s needed to overcome them.
News Medical interviewed Dr. Rajat Mukherjee, Statistician, and Director of Data Science at Cytel to investigate the potential of data science in clinical development.
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.
To close a clinical database right the first time you have to begin with study start-up. Clearly, you can’t close a database if the data is not cleaned and you can’t have clean data unless you know what is most important for analysis. It’s imperative that data management works closely with the statistics group during CRF/ eCRF design to ensure data is being collected and data checks are being written in a meaningful fashion. But that’s still not enough. The data should be cleaned on a regular basis and forms locked as soon as the data has been SDVd and reviewed. Even then, it will be important to have your statistics team run listings and tables early on to catch anything unexpected. If the data is cleaned and locked by the time the last patient visit comes around then getting Principal Investigator sign-off and ultimately closing the database can run much more smoothly and quickly.
Database lock is a significant milestone in the clinical trial, upon which further data analysis and reporting timelines depend. The Clinical Data Manager is responsible for steering the data management process to ensure that the database is locked on time, and correctly. In this blog we lay out the 6 steps to database lock success.
Statistical programmers are in high demand within the biopharmaceutical industry, and within the dynamic world of clinical trials the part they play is ever evolving. In this blog, we take a look at 5 trends which are shaping their roles in 2016 and beyond.
The explosion in healthcare information and “big data “has been one of the most written about topics in the last few years. These big data in the form of electronic health records, diagnostic tests, genomics, proteomics, not to mention data from wearable devices and apps have the potential to transform healthcare. That potential can only be realized though through the application of advanced analytics to recognize patterns from the vast information available. As such, disciplines such as pattern recognition play a pivotal role in the future of healthcare.