Gary has an MSc in Medical Statistics from the London School of Hygiene and Tropical Medicine and has worked in the Pharma industry since 1992 across several Statistical and Programming roles within the UK and globally. He was co-chair for PHUSE in 2020 and now is excited to be in the role of chair for the EU Connect 2021 conference. His current role is with PHASTAR, who he has been with since July 2019.

Mary received a MS degree in statistics from Iowa State University in 1989. She has been employed at Eli Lilly since 1989 and is currently a research advisor in the Safety Analytics group within the Statistical Sciences function.

Mary consults with compound teams on safetyanalysis planning for Phase 2-3 studies and integrated submission documents. Her primary interests include analyses of adverse event data, analyses of laboratory data, statistical analysis plans, and collection of analysis of suicide-related events.

Shiny (a web application framework for R) is an R package that makes it easy to build highly interactive web apps directly in R. It combines the computational power of R and modern visualisation techniques to create interactive applications. Harnessing this power, R users can develop Shiny apps for visualising clinical data, as well as applications that aid in study design and analysis. Shiny apps can empower non-statisticians to explore and visualise their data or perform their own analysis with methods we develop.

In this presentation we will try to understand what Shiny is, the basic structure of a Shiny app and how we can make our own Shiny app. We will look into some examples of Shiny applications that use the power of R and Shiny, for viewing the relationship between variables in multiple dimensions and altering our visualisation with real-time parameter refinement using the UI component of the app.

Biography:Jagadish Katam is a Principal Statistical Programmer at Princeps Technologies, working on end-to-end programming activities. He has more than nine years’ experience as an SAS programmer and in leading studies. He has worked on successful clinical submissions and has experience of working in therapeutic areas such as infectious diseases and oncology. Jagadish’s expertise ranges from SDTM, ADaM and TFLs to macro programming.

He likes to spend his free time in improving programming skills in SAS and exploring the usage of R in supporting innovative statistical methodologies and advanced visualisation in the pharma industry. Jagadish sees PHUSE as a good platform to share and gain knowledge on various innovative topics in the ever-evolving clinical industry.