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- 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

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Topic revision: r4 - 25 Feb 2016, FrankHarrell

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