Harrell’s primary mission is to combat . He argues against common but flawed practices like: Using P-values to select variables (Stepwise regression). Dropping "insignificant" variables from a final model.
It is dense. It assumes a solid foundation in statistics and familiarity with R (specifically the rms package). Regression Modeling Strategies: With Applicatio...
Categorizing continuous predictors (e.g., splitting age into groups). 🛠️ Key Technical Strengths Harrell’s primary mission is to combat
It bridges the gap between high-level theory and "boots-on-the-ground" data analysis. It teaches you how to build models that actually replicate in the real world. It is dense
🚀 If you want to stop just "running regressions" and start building robust, honest models, this is the most important book you will ever read.
A rigorous focus on bootstrapping for internal validation rather than simple data-splitting.