We are a network of researchers from UCL and beyond who use pharmacoepidemiology methods with secondary datasets.
This is our online knowledge hub - a resource for pharmacoepi researchers to share knowledge and code. We also host termly online events with expert speakers, research competitions & problem-solving workshops. Details of our next event can be found here.
🎯 These pages are currently in development - the below list shows which areas we have up and running (with links):
- Area for sharing code lists to identify clinical conditions, medications and other patient characteristics within electronic health record data
- Area for sharing re-usable code for data management and statistical analysis
- Information on different data sources
- Information on statistical methods used in pharmacoepi
We very much encourage contributions to these pages, in the name of open science for pharmacoepi! :) See our How to Contribute page for more information.
The use of big data sets, such as large UK primary care databases like Clinical Practice Research Datalink (CPRD) and The Health Improvement Network (THIN), is increasing among health researchers, as they provide a unique opportunity to work with large sample sizes (CPRD has over 50 million patients). Prescriptions are often one of the best coded areas in electronic health care records, and therefore they are growing in use amongst researchers to answer key health research questions. Pharmacoepi analysis in big datasets is often complex, with challenges unique to this area of data analysis, this includes: challenges with analysing dosing and duration, different preparations of medicines, understanding prescription use and compliance, establishing definitions for “long term” use of prescriptions. There is not a standard approach for preparing prescription data from electronic health records – this alone can often take months of trial and error.
 Annie Jeffery |
 Cini Bhanu |
 Sophie Eastwood |
 Alvin Richards-Belle |
You can directly reach out to the UCL Pharmacoepi Data Collaborative team by emailing [email protected].
This work is licensed under the MIT license (code) and Creative Commons Attribution 4.0 International license (for documentation). You are free to share and adapt the material for any purpose, even commercially, as long as you provide attribution (give appropriate credit, provide a link to the license, and indicate if changes were made) in any reasonable manner, but not in any way that suggests the licensor endorses you or your use, and with no additional restrictions. More information on licensing can be found here.
Thank you to the Alan Turing Institute's Reproducible Project Template to help shape these pages! And to Sophie Batchelor for support in getting the pages started!