MSCI 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