Streamlining clinical trial output workflows is a key challenge for clinical studies. Our project leverages Python to link the planned analysis stored in a google sheet LoPO (List of clinical study Planned Outputs) to the study scripts that generates SDTM/ADAM/TLG outputs. We also employ Snakemake, a powerful workflow management system, to automate creation of an execution plan that can then orchestrate the generation of output files from the processed data, using parallel computing.  To simplify the data collection process, we have created a Google Sheets add-on that allows statistical programming analysts to input clinical studies information directly.  Using the in-production LoPO tool as a case study, we will present learnings that have shaped our current best practices on writing, versioning, testing and deploying a Python package as a critical component of our clinical reporting workflow.
Daphné Grasselly
Roche
Learnings from deploying a Python package to orchestrate clinical trials outputs generation
Streamlining clinical trial output workflows is a key challenge for clinical studies. Our project leverages Python to link the planned analysis stored in a google sheet LoPO (List of clinical study Planned Outputs) to the study scripts that generates SDTM/ADAM/TLG outputs. We also employ Snakemake, a powerful workflow management system, to automate creation of an execution plan that can then orchestrate the generation of output files from the processed data, using parallel computing.  To simplify the data collection process, we have created a Google Sheets add-on that allows statistical programming analysts to input clinical studies information directly.  Using the in-production LoPO tool as a case study, we will present learnings that have shaped our current best practices on writing, versioning, testing and deploying a Python package as a critical component of our clinical reporting workflow.