Regression Modeling Strategies

With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis

Major Changes Since The First Edition

  1. Creation of a now mature R package, rms, that replaces and greatly extends the Design library used in the first edition
  2. Conversion of all of the book's code to R
  3. Conversion of the book source into knitr reproducible document
  4. All code from the text is executable and is on the web site
  5. Use of color graphics and use of the ggplot2 graphics package
  6. Scanned images were re-drawn
  7. New text about problems with dichotomization of continuous variables and with classification (as opposed to prediction)
  8. Expanded material on multiple imputation and predictive mean matching and emphasis on multiple imputation (using the Hmisc aregImpute function) instead of single imputation
  9. Addition of redundancy analysis
  10. Added a new section in Chapter 5 on bootstrap confidence intervals for rankings of predictors
  11. Replacement of the U.S. presidential election data with analyses of a new diabetes dataset from NHANES using ordinal and quantile regression
  12. More emphasis on semiparametric ordinal regression models for continuous Y, as direct competitors of ordinary multiple regression, with a detailed case study
  13. A new chapter on generalized least squares for analysis of serial response data
  14. The case study in imputation and data reduction was completely reworked and now focuses only on data reduction, with the addition of sparse principal components
  15. More information about indexes of predictive accuracy
  16. Augmentation of the chapter on maximum likelihood to include more flexible ways of testing contrasts as well as new methods for obtaining simultaneous confidence intervals
  17. Binary logistic regression case study 1 was completely re-worked, now providing examples of model selection and model approximation accuracy
  18. Single imputation was dropped from binary logistic case study 2
  19. The case study in transform-both-sides regression modeling has been reworked using simulated data where true transformations are known, and a new example of the smearing estimator was added
  20. Addition of 225 references, most of them published 2001-2014
  21. New guidance on minimum sample sizes needed by some of the models
  22. De-emphasis of bootstrap bumping for obtaining simultaneous confidence regions, in favor of a general multiplicity approach
Topic revision: r1 - 27 May 2015, FrankHarrell

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