Regression Modeling Strategies: With Applicatio... -

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

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. Regression Modeling Strategies: With Applicatio...

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

Extensive use of restricted cubic splines to let the data dictate the shape of relationships. He argues against common but flawed practices like:

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

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.

Heavy emphasis on multiple imputation rather than deleting rows.