Novartis

China: new region, new topics, new perspectives and new heights

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Enhancing Late-Stage Process Development with R

The value that R brings to clinical reporting is well established, and this has been the key factor in securing R's position within the pharma industry. However, as effective data collection, curation, and aggregation have increased across functions …

Introduction to R Implementation and Educational Initiatives for SAS Programmers

Take It, Build It and Own It

Rshiny app on Ph2b trial design based on MCP-Mod

Finding the right dose is a critical step in pharmaceutical drug development. There has been varies statistical methodology development for the design and analysis of clinical studies. In particular, MCP-Mod (Multiple Comparisons Procedure - …

Building Shiny Frameworks: Some Lessons

It is relatively simple to create a powerful visualization app using shiny, but what if you need to change your data wrangling process or wish to build a different output? How easy is it to provide this flexibility without having to rewrite the …

How to use a complete JavaScript toolchain in your Shiny development

Introduction to {shinyValidator}

Outstanding User Interfaces with Shiny

In recent years, R users' understanding of Shiny has greatly increased but so have client expectations. While one of Shiny’s greatest strengths is that it allows producing web applications solely from R code, meeting client’s more delicate …

Democratizing Shiny App Development: The Novartis DataPipeline Harmonized Framework

Motivated by the rapid rise in clinical data exploration, there is an increasing need to utilize interactive graphical displays using Shiny apps. To date, the development and deployment of study apps have required specialized knowledge and …