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

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?