Identification of subgroups with increased or decreased treatment effect is a challenging topic with several traps and pitfalls. In this project, we would like to establish good practices for subgroup identification, by building a simulation platform …
Statistical graphics play an important role in exploratory data analysis, model checking and diagnostics. The lineup protocol (Buja et. al 2009) enables statistical significance testing using visualizations, bridging the gap between exploratory and …
The Beatles rose to music fame in the 1960's and became a worldwide phenomenon. With millions of screaming fans and selling over 600 million records, they are often cited as one of the most influential rock bands in history. One reason for their fame …
Effective visual communication is a core competency for pharmacometricians, statisticians, and, more generally, any quantitative scientist. It is essential in every step of a quantitative workflow, from scoping to execution and communicating results …
metashiny is an R package that provides a point-and-click interface to quickly design, prototype, and deploy essential Shiny applications without having to write one single line of R code. The core idea behind metashiny is to parametrize 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 …
Scientists in drug discovery research utilize a wide variety of instrumentation and techniques to advance their research. While instrumentation vendors often provide software tools to deal with data wrangling and visualization, a simple collection of …
We define and illustrate a "deep visualization" paradigm for the analysis of a relatively large and complex clinical database for psoriasis (PSO) and psoriatic arthritis (PsA). This paradigm supports a growing number of machine learning and …
Introduction As pharmacometricians, we sometimes jump into complex modeling before thoroughly exploring our data. This can happen due to tight timelines, lack of ready-to-use graphic tools or enthusiasm for complex models. Exploratory plots can help …