BIOS 330 Syllabus

Numbers to the right of topics indicate sequential lecture numbers. Hn stands for Harrell Chapter n. Ln stands for lecture n.

  1. Introduction (H1) L1
    1. Course overview and logistics
    2. Course philosophy
    3. Hypothesis testing vs. estimation vs. prediction
    4. Examples of multivariable prediction problems
    5. Misunderstandings about classification vs. prediction
    6. Study planning considerations L2
    7. Choice of model
    8. Model uncertainty/data driven model selection/phantom d.f.
  2. General methods for multivariable models (H2) L3
    1. Notation for general regression models
    2. Model formulations
    3. Interpreting model parameters
    4. nominal predictors
    5. interactions
    6. Review of chunk tests
    7. Relaxing linearity assumption for continuous predictors
      1. avoiding categorization
      2. nonparametric smoothing
      3. simple nonlinear terms (L4)
      4. splines for estimating shape of regression function and determining predictor transformations
      5. cubic spline functions
      6. restricted cubic splines
      7. nonparametric regression (smoothers) (L5)
      8. advantages of splines over other methods
      9. recursive partitioning and tree models in a nutshell
    8. New directions in predictive modeling
    9. Tests of association (L6)
      1. Grambsch and O'Brien paper
    10. Assessment of model fit
      1. regression assumptions
      2. modeling and testing complex interactions
      3. interactions to prespecify
      4. distributional assumptions
  3. Missing data (H3, L7)
    1. Types of missing data
    2. Prelude to modeling
    3. Problems with alternatives to imputation
    4. Strategies for developing imputations
    5. Single imputation
    6. Multiple imputation
    7. Predictive mean matching (L8)
    8. The aregImpute algorithm
  4. Multivariable modeling strategy (H4)
    1. Pre-specification of predictor complexity
    2. Variable selection
    3. Overfitting and number of predictors
    4. Shrinkage
    5. Data reduction (H4.7, first page and summary chart, H14 up to H14.4, L8
    6. Overall modeling strategy
  5. Bootstrap, Validating and Describing the Model (H5)
    1. Bootstrap L9
    2. Model validation
  6. Describing the model L10
  7. R Multivariable Modeling/Validation/Presentation Software (H6, Alzola & Harrell 9.3-4) L11
  8. Case study in OLS regression (H7)
  9. Case study in data reduction and missing value imputation (H8 up until discussion of principal components) (H14.2,14.3)
  10. Project: Understanding interrelationships of predictor variables, dealing with missing data, developing and validating a multiple regression model using least squares Assigned Due
  11. Maximum Likelihood Estimation (H9 up until 9.3)
  12. Binary Logistic Model (H10)
    1. Model
    2. Odds ratios
    3. Special residual plots L33
    4. Applications of general methods
    5. Graphically presenting model L34
    6. Case studies
  13. Project: Develop and validate a binary logistic regression model Assigned 32 Due 35
  14. Proportional Odds Ordinal Logistic Models (H13.1-13.3) L36
    1. Model
    2. Odds ratios
    3. Applications of general methods
  15. Case study (H14-14.3) Assignment: Interpret an analysis that used a proportional odds ordinal logistic model Assigned 38 Due 41
  16. Brief Introduction to Survival Analysis (H16) L37
    1. Survival data and right-censoring
    2. log-rank test for comparing two groups
  17. Cox regression model (H19.1) L38
  18. Other Case Studies and Labs L40-41

  1. Maximum Likelihood Estimation (H9) L6
    1. Three test statistics (H6.3.3)
    2. Robust covariance matrix estimator (H9.5)
    3. Correcting variances for clustered or serial data using sandwich and bootstrap estimators (H9.5)
    4. Bootstrap simultaneous confidence regions using Tibshirani's bootstrap bumping (H9.7) L7
    5. R bootcov and rm.boot functions
    6. Simulations to study coverage of simultaneous bootstrap confidence regions
    7. Further use of the log likelihood (H9.8) L8
    8. Weighted MLE (H9.9)
    9. Penalized MLE (H9.10)
    10. Effective d.f. (H9.10)
    11. Tibshirani's lasso
  2. Ordinal Logistic Models (H13, 14) L9
    1. Models
    2. Using ordinal models and the Cox model for robust rank-based analysis of continuous response data
    3. Special residual plots
    4. Special use of penalized MLE
  3. Case study Project: Develop and validate a proportional odds ordinal logistic model
  4. Transform-both-sides Nonparametric Additive Regression Models (H15, L11)
  5. Generalized additive models
  6. ACE
  7. AVAS
    1. R `areg.boot` function
  8. Smearing estimator (H15.4) Project: Develop and interpret a nonparametric additive model for a continuous response
  9. Other topics such as cluster analysis, correspondence analysis, unsupervised association rules Final Project

-- FrankHarrell - 24 Dec 2012
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