Regression Modeling Strategies: With Applicatio... -

Categorizing continuous predictors (e.g., splitting age into groups). 🛠️ Key Technical Strengths

Extensive use of restricted cubic splines to let the data dictate the shape of relationships. Regression Modeling Strategies: With Applicatio...

Heavy emphasis on multiple imputation rather than deleting rows. Categorizing continuous predictors (e

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. Categorizing continuous predictors (e.g.

A rigorous focus on bootstrapping for internal validation rather than simple data-splitting.

It is dense. It assumes a solid foundation in statistics and familiarity with R (specifically the rms package).

🚀 If you want to stop just "running regressions" and start building robust, honest models, this is the most important book you will ever read.