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---+ BIOS 330 Syllabus %TOC% Numbers to the right of topics indicate sequential lecture numbers. Hn stands for Harrell Chapter n in the book's second edition. Ln stands for lecture n. ---++ Introduction (H1) L1 1 Course overview and logistics 1 Course philosophy 1 Hypothesis testing vs. estimation vs. prediction 1 Examples of multivariable prediction problems 1 Misunderstandings about classification vs. prediction (read [[http://fharrell.com/post/classification this]] also) 1 Study planning considerations 1 Choice of model 1 Model uncertainty/data driven model selection/phantom d.f. ---++ General methods for multivariable models (H2) L2 1 Notation for general regression models 1 Model formulations 1 Interpreting model parameters 1 nominal predictors 1 interactions 1 Review of chunk tests 1 Relaxing linearity assumption for continuous predictors 1 avoiding categorization - see also [[http://hbiostat.org/doc/bbr.pdf BBR Sections 18.3.2-18.3.3]] 1 nonparametric smoothing 1 simple nonlinear terms (L3) 1 splines for estimating shape of regression function and determining predictor transformations 1 cubic spline functions 1 restricted cubic splines 1 see interactive demos of spline fitting and continuity [[https://pclambert.net/interactivegraphs here]] 1 nonparametric regression (smoothers) 1 advantages of splines over other methods 1 recursive partitioning and tree models in a nutshell 1 Bayesian spline modeling: watch [[https://youtu.be/ENxTrFf9a7c?t=2228][McElreath's presentation]] 1 New directions in predictive modeling (L4) 1 Tests of association 1 Grambsch and O'Brien paper 1 Assessment of model fit 1 regression assumptions 1 modeling and testing complex interactions 1 interactions to prespecify 1 distributional assumptions ---++ Missing data (H3, L5) 1 Types of missing data 1 Prelude to modeling 1 Missing values for different types of response variables 1 Problems with alternatives to imputation 1 Strategies for developing imputation models 1 Single imputation 1 Predictive mean matching 1 Multiple imputation 1 The =aregImpute= algorithm (L6) 1 Diagnostics 1 Summary and rough guidelines; effective sample size ---++ Multivariable modeling strategy (H4) 1 Pre-specification of predictor complexity 1 Variable selection 1 Sample size, overfitting, and number of predictors (L7); also see [[http://fharrell.com/post/ordinal-info this]] 1 Shrinkage 1 Collinearity 1 Data reduction 1 Overly influential observations (L8) 1 Comparing two models 1 Improving the practice of multivariable prediction 1 Overall modeling strategies ---++ Bootstrap, Validating, Describing, and Simplifying the Model (L9, H5) 1 Describing the fitted model 1 Bootstrap 1 Model validation 1 Bootstrapping ranks of predictors (L10) 1 Simplifying the model by approximating it 1 How do we break bad habits? ---++ R Multivariable Modeling/Validation/Presentation Software (H6, BBR9) ---++ Case Study in Longitudinal Data Modeling with Generalized Least Squares (H7, L11) 1 Notation and model for mean time-response profile 1 Keeping baseline variables as baseline 1 Modeling within-subject dependence 1 Overview of competing methods for serial data 1 Checking model fit 1 Software 1 Case study from a randomized trial ---++ Case study in data reduction (H8, L12) 1 How many parameters can be estimated? 1 Redundancy analysis 1 Variable clustering 1 Transformation/scaling of variables using =transcan= 1 Principal components Cox regression 1 Sparse principal components 1 Nonparametric transform-both-sides regression for transforming/scaling variables ---++ Maximum Likelihood Estimation (H9, L13) 1 Three test statistics 1 Robust covariance matrix estimator 1 Correcting variances for clustered or serial data using sandwich and bootstrap estimators 1 Confidence regions 1 Wald (large-sample normal approximation) 1 Bootstrap 1 Simultaneous (normal approx) 1 General contrasts through differences in linear predictor 1 Further use of the log likelihood 1 Weighted MLE 1 Penalized MLE 1 Effective d.f. ---++ Binary Logistic Model (H10, L15) 1 Model 1 Odds ratios, risk ratios, and risk differences 1 Detailed example 1 Estimation 1 Test statistics 1 Residuals 1 Assessment of model fit 1 Quantifying predictive ability 1 Validating the model 1 Describing fitted models 1 R functions ---++ Binary Logistic Case Study 1 (H11, L16) ---++ Binary Logistic Case Study 2 (H12, L17) ---++ Ordinal Logistic Models (H13, L18) 1 Ordinality assumption 1 PO Model 1 Model 1 Assumptions, interpretations of parameters, estimation, residuals 1 Assessment of fit 1 Predictive ability measures 1 Describing the model 1 Validation 1 R functions 1 CR Model 1 Model 1 Assumptions, interpretation of parameters, estimation, residuals 1 Assessment of fit 1 Extended CR model including penalization 1 Validation 1 R functions ---++ Ordinal Logistic Regression Case Study (H14, L19) ---++ Case Study in Ordinal Regression for Continuous Univariate Y (H15, L21-22) 1 No transformation satisfying all linear model assumptions exists for the dataset 1 Assumptions of the proportional odds ordinal logistic model (semiparametric model) are not satisfied 1 Development and validation of a quantile regression model for median glycohemoglobin 1 Failure of linear multiple regression 1 Failure of proportional odds model for continuous gh 1 Comparison with quantile regression 1 Obtaining many types of predicted values ----++ Transform-both-sides Nonparametric Additive Regression Models (H16, L22-23) 1 Generalized additive models 1 ACE 1 AVAS 1 Parametric approach 1 Obtaining estimates on the original scale 1 Smearing estimator 1 R =areg.boot= function 1 Examples ---++ Some Components of Survival Analysis and Parametric Survival Models (H17-H18, L24) ---++ Parametric Survival Model Case Study (H19, L25) ---++ Cox Model Case Study (H20, L26) ---++ Analysis of Covariance in Randomized Trials ([[http://fharrell.com/doc/bbr.pdf][BBR]] Chapter 13, L27) ---++ Medical Diagnostic Research ([[http://fharrell.com/doc/bbr.pdf][BBR]] Chapter 19, L28)
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Topic revision: r53 - 17 Feb 2019,
FrankHarrell
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