The underlying drivers

The growth of MDAS

MDAS within Roche

Data types in

Personalised Health Care (PHC)

The expansion of toolsets

The infrastructure we used has evolved

Admin managed

r.roche.com

Epidemiologists/

Analysts/

Statisticians

Data Scientists

The challenges

• Pharma is traditionally bad at sharing code
  • Code shares can be file servers or wikis...
  • Sharing code is seen as a risk
  • We need to share and collaborate
• Data Scientists need to be flexible
  • Match the languages used in each analysis to the methods and data types
  • There is a lot of training overhead in being a polygot
  • Infra code should be centralised

Step 1: Wrap infrastructure in a shared code base

Abstracting infrastructure

RWDSverse

RocheTemplates

Making it easy for scientists to derive insights from the Flatiron data

For humans

No SQL (dbplyr)

Reuse code for variable definitions, tables, plots

Vignette driven docs

Robust science

Embedded provenance

Extensible (by users)

Automated checks of data

Code unit test coverage high

Step 2: Share, colloborate and play

Collaborative community