Data Science Solution to Event Prediction in Outsourced Clinical Trial Models

Stud Health Technol Inform. 2015:216:1065.

Abstract

Late phase clinical trials are regularly outsourced to a Contract Research Organisation (CRO) while the risk and accountability remain within the sponsor company. Many statistical tasks are delivered by the CRO and later revalidated by the sponsor. Here, we report a technological approach to standardised event prediction. We have built a dynamic web application around an R-package with the aim of delivering reliable event predictions, simplifying communication and increasing trust between the CRO and the in-house statisticians via transparency. Short learning curve, interactivity, reproducibility and data diagnostics are key here. The current implementation is motivated by time-to-event prediction in oncology. We demonstrate a clear benefit of standardisation for both parties. The tool can be used for exploration, communication, sensitivity analysis and generating standard reports. At this point we wish to present this tool and share some of the insights we have gained during the development.

MeSH terms

  • Adverse Drug Reaction Reporting Systems / organization & administration*
  • Clinical Trials as Topic / statistics & numerical data*
  • Computer Simulation
  • Drug Monitoring / methods*
  • Drug-Related Side Effects and Adverse Reactions / diagnosis
  • Drug-Related Side Effects and Adverse Reactions / epidemiology*
  • Electronic Health Records / classification
  • Electronic Health Records / statistics & numerical data*
  • Humans
  • Incidence
  • Models, Statistical
  • Outsourced Services / statistics & numerical data*
  • Risk Assessment / methods
  • Software
  • United Kingdom / epidemiology