Abstract: Supporting data-driven decisions in the planning of clinical trials during the current pandemic involves extensive integration of heterogenous data sources, sophisticated predictive modelling, and custom visualization to communicate the predictions to decision makers. We used R to rapidly deliver end-to-end planning tools for GSK in this difficult time. We built a pipeline to integrate, clean and, crucially - test, a variety of internal and external datasets. This data then fed into a patient recruitment model and, finally, into a SQL-powered shiny app for interactive visualizations. The creation of the planning tool required bringing together statisticians, data scientists and clinical operations in an intense collaboration, powered by R.
Speaker: Christina Fillmore
Title: Using R to Create an End to End Process for Predicting Delays in Recruitment from Covid-19
Abstract_Sanitzed: Supporting data-driven decisions in the planning of clinical trials during the current pandemic involves extensive integration of heterogenous data sources, sophisticated predictive modelling, and custom visualization to communicate the predictions to decision makers. We used R to rapidly deliver end-to-end planning tools for GSK in this difficult time. We built a pipeline to integrate, clean and, crucially - test, a variety of internal and external datasets. This data then fed into a patient recruitment model and, finally, into a SQL-powered shiny app for interactive visualizations. The creation of the planning tool required bringing together statisticians, data scientists and clinical operations in an intense collaboration, powered by R.