Cytel Blog

In this blog we share a case study of how we established and ramped up a functional service outsourcing partnership for biostatistics, programming and data management.

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. 

To mark the occasion of our 30th anniversary, in late 2017 we conducted a brief survey to gain a snapshot of what professionals in data management, statistical programming, and biostatistics feel are the key challenges facing their functions, the top areas for skills development, and the clinical data areas they believe are likely to have the greatest impact on drug development.

We are now able to share our findings- read on for some highlights, and to download your complimentary copy of the report.

Photo by Nghia Le on Unsplash

CliPLab (Clinical Professional Laboratory) is Cytel’s premier training initiative for bridging the skills gap in biometrics and analytics within clinical development. Leveraging Cytel’s experience and reputation in biostatistics and clinical biometrics, the organization provides practical learning modules in clinical SAS programming, biostatistics, data management, pharmacovigilance, scientific medical writing, key therapeutic areas and crucial soft skills. With learning programs led by experienced trainers, CliPLab engages both with individual students, and companies (e.g. biopharma, CROs) needing to support and augment their internal training programs.

In this blog we share a case study of how CliPLab supported a pharmaceutical customer with a tailored SAS training program for a cohort of new graduate recruits. 

Our Career Perspectives' series is back! 

Cytel has industry-leading experts in statistical programming with years of SAS^® Programming expertise, combined with in-depth knowledge of specific clinical subject matter, which allows for competent and on-time completion of tasks. Our extensive service offering includes CDISC migration, mapping to SDTM and statistical programming.

In the first blog of 2018, we talk to Lisa, who is based in Boston, to find out more on her career path, achievements, current role at Cytel and her interests outside of work.

PharmaSUG 2017 proved to be an inspirational and informative event. With over 200 paper presentations, posters, and hands-on workshops to choose from, delegates could select the topics most relevant to their statistical programming experience and interests.

PharmaSUG papers are split into 15 different academic sections, and Cytel was well represented in the Statistics and Pharmacokinetics stream with two of our talented programmers, Chris Smith and Sharmeen Reza, presenting separate papers.

We are delighted to announce that Chris Smith, Senior Statistical Programmer was awarded best paper in his stream. The winning paper Multiple Imputation: a Statistical Programming Story was co-authored with industry colleague Scott Kosten.

 In a previous blog, we provided an overview of basic data structures in R.  In this follow up piece, we will provide a snapshot of basic syntax in R for programmers who want to get up to speed in this increasingly important programming language. 

R is on the rise in biopharma, and as we have previously discussed on the blog, it is now time for SAS programmers to get up to speedwith this popular and powerful programming language.  Indeed, one of the advantages of R is its ability to integrate with other languages like C, C++, Python and SAS. Its strong graphical capabilities allow output in PDF, JPG, PNG, and SVG formats and table outputs for LaTeX and HTML. Importantly, as an open source resource, there is a strong community around R and extensive support for users in the form of forums like R-bloggers, StackOverflow and GitHub Repository

In this blog, we’ll provide an overview of R basic structures for programmers.  

Nonlinear Mixed Effects Modeling (NONMEM) is a type of population pharmacokinetics/pharmacodynamics (popPK/PD) analysis used in Clinical Pharmacology research. The population PK approach combined with pharmacodynamics modeling, allows integrated analysis, interpretation, and prediction of the drug’s safety, efficacy, dose-concentration relationship, and dosing strategy.