RMS Additional Resources

Regression Modeling Strategies: Additional Resources

  1. Library of methods and applications papers provided to course participants
  2. Regression and Other Stories, Gelman, et al, (2021).
  3. Statistical Rethinking: A Bayesian Course with Examples in R and STAN. Richard McElreath (2020)
  4. Pearl, Judea and Mackenzie, Dana: The book of why: the new science of cause and effect (2018)
  5. Fundamentals of Modern Statistical Methods: Substantially Improving Power and Accuracy. Wilcox, Rand (2010)
  6. Uncertainty: The Soul of Modeling, Probability & Statistics. Briggs, William (2016)
  7. Scientific Reasoning: The Bayesian Approach. Howson & Urbach (2006; 3rd ed.)
  8. Flexible Imputation of Missing Data. Stef van Buuren (2018; 2nd ed.)
  9. Applied Missing Data Analysis. Craig K. Enders (2010)
  10. 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.
  11. Anything&Everything by Sander Greenland: such as, When Should Epidemiologic Regressions Use Random Coefficients? (2004)
  12. 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]
  13. Anything&Everything by Stephen Senn: such as Being Just about Adjustment (2020); and Balance is not the issue (2020).
  14. Ewout Steyerberg “Predictive Analytics” Population Health: Predictive Analytics | Coursera
  15. Resources for Bayesian modeling and design
  16. Bernoulli’s Fallacy by Aubrey Clayton
  17. Papers on simulation
3 Likes

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?