Numbers to the right of topics indicate sequential lecture numbers.
Hn stands for Harrell Chapter n. Ln stands for lecture n.
Introduction (H1) L1
Course overview and logistics
Course philosophy
Hypothesis testing vs. estimation vs. prediction
Examples of multivariable prediction problems
Misunderstandings about classification vs. prediction
Study planning considerations L2
Choice of model
Model uncertainty/data driven model selection/phantom d.f.
General methods for multivariable models (H2) L3
Notation for general regression models
Model formulations
Interpreting model parameters
nominal predictors
interactions
Review of chunk tests
Relaxing linearity assumption for continuous predictors
avoiding categorization
nonparametric smoothing
simple nonlinear terms (L4)
splines for estimating shape of regression function and determining predictor transformations
cubic spline functions
restricted cubic splines
nonparametric regression (smoothers) (L5)
advantages of splines over other methods
recursive partitioning and tree models in a nutshell
New directions in predictive modeling
Tests of association (L6)
Grambsch and O'Brien paper
Assessment of model fit
regression assumptions
modeling and testing complex interactions
interactions to prespecify
distributional assumptions
Missing data (H3, L7)
Types of missing data
Prelude to modeling
Problems with alternatives to imputation
Strategies for developing imputations
Single imputation
Multiple imputation (L8)
Predictive mean matching
The aregImpute algorithm
Multivariable modeling strategy (H4, L9)
Pre-specification of predictor complexity
Variable selection
Overfitting and number of predictors
Shrinkage (L10)
Data reduction
Overall modeling strategy (L12)
Bootstrap, Validating, Describing, and Simplifying the Model (H5)
Bootstrap
Model validation
Describing and interpreting the fitted model
Model approximation
R Multivariable Modeling/Validation/Presentation Software (L13, H6, Alzola & Harrell 9.3-4)
Case study in OLS regression (H7)
Case study in data reduction and missing value imputation (H8 up until discussion of principal components) (H14.2,14.3)
Project: Understanding interrelationships of predictor variables, dealing with missing data, developing and validating a multiple regression model using least squares Assigned Due
Maximum Likelihood Estimation (H9 up until 9.3)
Binary Logistic Model (H10)
Model
Odds ratios
Special residual plots L33
Applications of general methods
Graphically presenting model L34
Case studies
Project: Develop and validate a binary logistic regression model Assigned 32 Due 35