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
Hypothesis testing vs. estimation vs. prediction
Examples of multivariable prediction problems
Study planning considerations
Choice of model
General methods for multivariable models (H2)
Notation for general regression models L13
Model formulations
Interpreting model parameters
nominal predictors
interactions
Relaxing linearity assumption for continuous predictors L14
nonparametric smoothing
simple nonlinear terms
splines for estimating shape of regression function and determining predictor transformations
cubic spline functions
restricted cubic splines
advantages of splines over other methods such as nonparametric regression
recursive partitioning and tree models in a nutshell
Tests of association L18
Assessment of model fit
regression assumptions
modeling and testing interactions
Missing data (H3)
Types of missing data L19
Prelude to modeling
Problems with alternatives to imputation
Strategies for developing imputations
Single imputation
Multiple imputation
Multivariable modeling strategy (H4) L21
Pre-specification of predictor complexity
Variable selection L22
Overfitting and number of predictors
Shrinkage
Data reduction (H4.7, first page and summary chart, H14 up to H14.4, L23
Overall modeling strategy L24
Bootstrap, Validating and Describing the Model (H5)
Bootstrap L25
Model validation L26
Describing the model L27
R Multivariable Modeling/Validation/Presentation Software (H6, Alzola & Harrell 9.3-4) L28
Case study in OLS regression (H7) L29
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 23 Due 28
Maximum Likelihood Estimation (H9 up until 9.3) L30
Binary Logistic Model (H10) L30
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