Introduction to the Use of Electronic Medical Records (EHR) Data for Health Technology Assessment (HTA)
Session Dates: July 13-14, 2022, virtual
Electronic health records (EHRs) are increasingly available for health economics and outcomes research yet are unfamiliar to many researchers and analysts. This introductory course will focus on principles for understanding the EHR and how it may be used for research and inform Health Technology Assessments.
Scott Ramsey, MD, PhD
Director, Hutchinson Institute for Cancer Outcomes Research
& Professor, Fred Hutchinson Cancer Center
Seattle, WA, USA
Shrujal Baxi, MD, MPH
Senior Vice President Clinical Science
San Francisco, CA, USA
Tools for Reproducible Real-World Data (RWD) Analyses
Past sessions: May 28, 2022 at University of York; May 24-25, 2021 virtual; Nov 2, 2019 in Copenhagen; May 18, 2018 in New Orleans; Nov 7, 2018 in Barcelona
This course focuses on the concepts and tools of reproducible research and reporting of modern data analyses. The need for more reproducible tools in health economics and outcomes research is growing rapidly as analyses of real world data become more frequent, involve larger datasets, and employ more complex computations.
We cover the principles of structuring and organizing a modern data analysis, literate statistical analysis tools, formal version control, software testing and debugging, and developing reproducible reports. Numerous real-world examples and an interactive class exercises reinforce the concepts and tools introduced. RStudio Cloud is used for exercises. Participants who wish to gain hands-on experience should bring their laptops.
- What is reproducible research?
- Why is reproducibility so important?
- How do we get there?
- Sharing code & findings
- Writing clear code
- Organizing data
- Catching mistakes
The materials for in-class exercises and examples are here (the password is provided to registered students). The dataset used for teaching is a simple dummy version of electronic health records (EHR)-derived data for a set of cancer patients receiving treatment. The R code for analysis exercises is available on GitHub.
coding style & culture
- Writing system software: code comments
- Style guide from Google
- The Tidyverse style guide
- Software Carpentry: best practices for writing R code
- Code review best practices
- RStudio webinars
- RStudio cheat sheets
- R for Data Science textbook (also available as paperback via Amazon)
- Advanced R programming (covers many advanced topics)
- Good Practices for Real-World Data Studies of Treatment and/or Comparative Effectiveness: Recommendations from the Joint ISPOR-ISPE Special Task Force on Real-World Evidence in Health Care Decision Making
- Reproducibility checklist
- rOpenSci – a non-profit website that fosters reproducible research
- Coursera Reproducible Research