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. Hypothesis testing vs. estimation vs. prediction
    3. Examples of multivariable prediction problems
    4. Study planning considerations
    5. Choice of model
  2. General methods for multivariable models (H2)
    1. Notation for general regression models L13
    2. Model formulations
    3. Interpreting model parameters
    4. nominal predictors
    5. interactions
    6. Relaxing linearity assumption for continuous predictors L14
      1. nonparametric smoothing
      2. simple nonlinear terms
      3. splines for estimating shape of regression function and determining predictor transformations
      4. cubic spline functions
      5. restricted cubic splines
      6. advantages of splines over other methods such as nonparametric regression
      7. recursive partitioning and tree models in a nutshell
    7. Tests of association L18
    8. Assessment of model fit
    9. regression assumptions
    10. modeling and testing interactions
  3. Missing data (H3)
    1. Types of missing data L19
    2. Prelude to modeling
    3. Problems with alternatives to imputation
    4. Strategies for developing imputations
    5. Single imputation
    6. Multiple imputation
  4. Multivariable modeling strategy (H4) L21
    1. Pre-specification of predictor complexity
    2. Variable selection L22
    3. Overfitting and number of predictors
    4. Shrinkage
    5. Data reduction (H4.7, first page and summary chart, H14 up to H14.4, L23
    6. Overall modeling strategy L24
  5. Bootstrap, Validating and Describing the Model (H5)
    1. Bootstrap L25
    2. Model validation L26
  6. Describing the model L27
  7. R Multivariable Modeling/Validation/Presentation Software (H6, Alzola & Harrell 9.3-4) L28
  8. Case study in OLS regression (H7) L29
  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 23 Due 28
  11. Maximum Likelihood Estimation (H9 up until 9.3) L30
  12. Binary Logistic Model (H10) L30
    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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Topic revision: r2 - 25 Dec 2012, FrankHarrell
 

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