Biostatistics II

Concepts to Understand

  • Advantages of regression modeling over stratification and matching
  • Variety of purposes of regression models
  • Assumptions of linear additive models
  • Methods for checking these assumptions
  • Global vs. partial tests of association
  • Multiple ways of computing test statistics in multiple regression models that were fitted using ordinary least squares
  • Indicator variables and how their corresponding regression coefficients are interpreted; relationship to ANOVA
  • Interpretation of interaction effects
  • Differential treatment effects (heterogeneous treatment effects)
  • ANCOVA in RCTs
  • Writing null hypotheses precisely in terms of parameters being tested
  • Understanding tests for the overall association of a predictor with the response, and how to test sub-hypotheses such as linearity
  • Combined partial tests for multiple predictors
  • Combined tests for overall effects of a predictor when it interacts with other predictors
  • Regression splines (linear, cubic, and restricted cubic) and knots
  • Problems with naive approaches of handing missing data
  • Initial understanding of multiple imputation
  • The effect of changing how models are fitted based on looking at the data
  • Deciding on the number of degrees of freedom to "spend" in a model, and where to spend them
  • Have an initial understanding of data reduction
  • Model validation approaches and which methods of validation are most stringent; internal vs. external validation
  • How to display a complex regression model
  • Exact interpretation of logistic model coefficients in the linear case
  • Assumptions of binary logistic regression
  • How to convert between probabilities, odds, and log odds
  • Measures of predictive accuracy and predictive ability for binary logistic models
  • What is meant by an ordinal response variable and that there are models explicitly for ordinal responses
Topic revision: r4 - 25 Feb 2016, FrankHarrell
 

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