Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis

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Accurate and efficient regression modeling for microarchitectural performance and power prediction (2006, methodology): “Although commonly used, stepwise regression has several significant biases cited by Harrell [ 4 ].”
Low-Level Environmental Lead Exposure and Children’s Intellectual Function: An International Pooled Analysis (2005, methodology): “After an initial model was selected, the tests of linearity and the restricted cubic spline models were recomputed to ensure that our initial model was still appropriate after adjustment for covariates ( Harrell 2001 ).”
External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges (2016, background): “Discrimination is usually measured by the C statistic, 18 and for survival outcomes also the D statistic (box 1).”
Accounting for animal movement in estimation of resource selection functions: sampling and data analysis. (2009, methodology): “We propose as an easily implemented solution the use of linear splines of ral (Harrell 2001:18).”
What You See May Not Be What You Get: A Brief, Nontechnical Introduction to Overfitting in Regression-Type Models (2004, methodology): “Advanced computing also has allowed the relatively easy calculation of even more complex shrinkage algorithms, such as maximum likelihood penalization (see Harrell [3], pp.”