- Library of methods and applications papers provided to course participants
- Regression and Other Stories, Gelman, et al, (2021).
- Statistical Rethinking: A Bayesian Course with Examples in R and STAN. Richard McElreath (2020)
- Pearl, Judea and Mackenzie, Dana: The book of why: the new science of cause and effect (2018)
- Fundamentals of Modern Statistical Methods: Substantially Improving Power and Accuracy. Wilcox, Rand (2010)
- Uncertainty: The Soul of Modeling, Probability & Statistics. Briggs, William (2016)
- Scientific Reasoning: The Bayesian Approach. Howson & Urbach (2006; 3rd ed.)
- Flexible Imputation of Missing Data. Stef van Buuren (2018; 2nd ed.)
- Applied Missing Data Analysis. Craig K. Enders (2010)
- Modern Epidemiology (Third Edition) Kenneth J. Rothman, Timothy L. Lash, Sander Greenland (2008); especially chapters 20 & 21: Introduction to Regression Models, and Introduction to Regression Modeling, by Sander Greenland.
- Anything&Everything by Sander Greenland: such as, When Should Epidemiologic Regressions Use Random Coefficients? (2004)
- Modern Epidemiology (Fourth Edition) by Timothy L. Lash, Tyler J. VanderWeele, Sebastien Haneuse, Kenneth J. Rothman (2021); especially Part III, Data Analysis [including various particular regression modeling topics]
- Anything&Everything by Stephen Senn: such as Being Just about Adjustment (2020); and Balance is not the issue (2020).
- Ewout Steyerberg “Predictive Analytics” Population Health: Predictive Analytics | Coursera
- Resources for Bayesian modeling and design
- Bernoulli’s Fallacy by Aubrey Clayton
- Papers on simulation
This is great, Frank. Thanks so much. The last link, Papers for simulation, requires a login and password to the hbiostat site. Can you share that info, or post the papers to another repository that doesn’t require the login?