Fast, flexible and user-friendly

BaseCase is the leading value communication platform for the life sciences industry. It is the fastest way to integrate complex evidence—such as health economic models, real world datasets, and slide decks—into engaging mobile content. BaseCase is also proven to reduce costs and shorten timelines when developing mobile tools.

  • Update your content in the cloud, at any time
  • Streamlined global deployments
  • Rock-solid security
Introducing BaseCase Data Studio

BaseCase Data Studio allows you to include live statistical modeling and advanced data analysis in your BaseCase apps using R. This enables you to create powerful interactive tools easily, by linking script inputs and outputs with the presentation layer.

Using Data Studio, you can mix script outputs with arbitrary spreadsheet formulas to process and present data however you need. R-based resources can also be updated and modified easily—and deployed globally in just a few clicks.

Commonly used R packages in the life sciences industry, such as heemod and deSolve, are supported “out-of-the-box”.

Why use BaseCase Data Studio?

Share data-driven content centrally and securely, across devices

With Data Studio, users can easily share R-based mobile content across devices and markets. It also offers built-in version control, allowing users to release or roll-back R-based resources as necessary.

Advance your data analysis

Take data analysis one step further, with R providing more advanced formulas than Excel. What’s more, live statistical analysis and data simulation can be performed on the spot during each presentation.

Tell the story behind your data

Process and present data in whichever way you need through mixing script outputs with arbitrary spreadsheet formulas. Users can also leverage unique design elements to create compelling interactive tools, linking the script inputs and outputs to the presentation layer.

Example tools created with Data Studio

Probabilistic Sensitivity Analysis: A tool that performs a probabilistic sensitivity analysis during a payer engagement to display the different outcomes of a health economic model.

Discrete-Event Simulation: Running multiple discrete-event simulation models to assess the cost-effectiveness of a treatment.

Predictive Analytics: Leveraging predictive analytics to predict length-of-stay (LOS) and other health utilization parameters in a hospital setting.