Reading List — EPID 785R
Note that these readings are recommended but not required. However, they were carefully selected to be of the most benefit to you as you navigate the concepts in this course. The intention is that they will continue to serve you as you build your knowledge of these topics.
Importantly, do not use these as a replacement for the course notes. You are primarily responsible for the material covered in the weekly course notes.
You are encouraged to read this material, and bring any questions about it to the lab, lectures, or slack channel.
Additional readings will be posted as the course progresses.
Week 0: Math Foundations
Deisenroth MP, Faisal AA, Ong CS (2020) Mathematics for Machine Learning. Cambridge University Press. https://mml-book.com
Week 1: Randomized Controlled Trials & Emulation
Greenland S and Robins JM. Identifiability, Exchangeability, and Epidemiologic Confounding. International Journal of Epidemiology 1986;15:412-18 [DOI]
Hernán MA, et al. Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease. Epidemiology 2008;19(6):766-79 [DOI]
Hernán MA and Robins JM. Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available. American Journal of Epidemiology 2016;183(8):758-764. [DOI]
Cashin AG, et al. Transparent Reporting of Observational Studies Emulating a Target Trial—The TARGET Statement. JAMA 2025;334(12):1084-1093 [DOI]
Week 2: Data Collection
Young JG, Stensrud MJ, Tchetgen Tchetgen EJ, Hernán MA. A Causal Framework for Classical Statistical Estimands in Failure-Time Settings with Competing Events. Statistics in Medicine. 2020;8:1199--1236. [DOI]
Rudolph JE, Lesko CR, Naimi AI. Causal Inference in the Face of Competing Events. Current Epidemiology Reports 2020;7(3):125--131. [DOI]
Lau B, et al. Competing risk regression models for epidemiologic data. American Journal of Epidemiology 2009;170(2):244–256. [DOI]
Week 3: Outcome Dependent Sampling
O'Brien KM et al. The Case for Case-Cohort: An Applied Epidemiologist's Guide to Reframing Case-Cohort Studies to Improve Usability and Flexibility. Epidemiology 2022;33(3):354-361. [DOI]
Zhou H et al. Outcome-Dependent Sampling: An Efficient Sampling and Inference Procedure for Studies with a Continuous Outcome. Epidemiology 2007;18(4):461-8. [DOI]
Lash TL and Rothman KJ. Case-Control Studies. In: Modern Epidemiology, 4th Ed. Chapter 8, pages 161-84. Eds. Lash TL, VanderWeele TJ, Haneuse S, Rothman KJ. 2021. Wolters Kluwer.
Week 4: Regression as a Toolkit: The Descriptive-Predictive-Causal Framework
Carlin JB and Moreno-Betancur M. On the Uses and Abuses of Regression Models: A Call for Reform of Statistical Practice and Teaching. Statistics in Medicine 2025;44(13-14):e10244 [DOI]
Greenland S. Some Ways to Make Regression Modeling More Helpful Than Misleading. Statistics in Medicine 2025;44(13-14):e10313 [DOI]
Week 5: The Anatomy of a Regression Model
Clark M and Berry S. Models Demystified: A Practical Guide from Linear Regression to Deep Learning. Chapters 2, 3, and 4: https://m-clark.github.io/book-of-models/
Week 6: Generalized Linear Models
Clark M and Berry S. Models Demystified: A Practical Guide from Linear Regression to Deep Learning. Chapter 8: https://m-clark.github.io/book-of-models/generalized_linear_models.html
Week 7: Variance Estimation
Mansournia MA et al. Reflection on modern methods: demystifying robust standard errors for epidemiologists. International Journal of Epidemiology 2021;50(1):346-351 [DOI]
Week 8: Conditional vs. Marginal Adjustment
Naimi AI and Whitcomb BW. Estimating Risk Ratios and Risk Differences Using Regression. Am J Epidemiol 2020;189(6):508-510 [DOI]
Daniel R et al. Making apples from oranges: Comparing noncollapsible effect estimators and their standard errors after adjustment for different covariate sets. Biometrical J. 2021;63:528-57. [DOI]
Week 9: Flexible Regression
TBD
Week 10: Penalized Regression and Model Evaluation
TBD
Week 11: Regression for Outcome-Dependent Sampling
TBD
Week 12: Survival Analysis: Concepts
Hernán MA. The Hazards of Hazard Ratios. Epidemiology 2010;21(1):13–15. [DOI]
Week 13: Survival Analysis: Parametric and Semiparametric Models
Stensrud MJ and Hernán MA. Why Test for Proportional Hazards? JAMA 2020;323(14):1401–1402 [DOI]