6  Resources

All lab members are invited to expand these lists. Please correct or report any broken links or other errors.

6.1 Well-being

Many students deal with stress related to classes or research, stress from outside factors, and/or mental health issues. Whatever you’re going through, you aren’t alone. You owe it to yourself to get the support you need. Please feel comfortable coming to me (Alton) if you think I can help with something. Also consider these resources:

• McGill wellness hub
• Resources for crisis/emergencies
• Peer Support Centre at McGill

6.2 Programming

• How to use Git/Github with R blog post by David Keyes on Rfortherestofus.com
• Happy Git and Github for the useR a more extensive e-book
• “Putting the R into Reproducible Research” (2019) by Dr Anna Krystalli
• Project-oriented workflow (2017) by Jenny Bryan, 2017
• Markdown Cheatsheet by adam-p on github
• Good enough practices in scientific computing (2017) article from PLOS Computational Biology by Wilson et. al.

6.3 Writing

• Writing your first academic paper (2016) is a helpful guide by Jeff Leek, a biostatistician at Johns Hopkins.
• The 12 steps to writing a paper and staying sane (2014) from the Health Research Journey blog by Jodie Oliver-Baxter and Lynsey Brown

6.4 Methods

6.4.1 Cost-effectiveness/health technology assessment

• Cost-Effectiveness in Health and Medicine (2016) is considered ‘the Bible’ of CEA

6.4.2 Statistical methods

• Statistical Rethinking book, course, and exercises by Richard McElreath
• Value of Information Analysis in Models to Inform Health Policy, review paper by Christopher H. Jackson, Gianluca Baio, Anna Heath, Mark Strong, Nicky J. Welton, and Edward C.F. Wilson
• Visual explainer for multi-level modeling

6.4.3 Data science / Machine learning

• BIMS 8382 Intro to Biomedical Data Science is a course by Stephen Turner at the UVA School of Medicine with great lecture notes and R code

• Data Science from Stratch (Sean Kross) is a helpful list of resources

• Machine Learning Methods That Economists Should Know About (2019), review paper by Susan Athey and Guido W. Imbens

6.4.4 Other

• Decision Modeling (2022), a free PDF book by David M. Tulett, is a great resource on applying optimization and mathematical modeling to decision problems.