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MsciBiostatIIConcepts
(25 Feb 2016,
FrankHarrell
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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
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Topic revision: r4 - 25 Feb 2016,
FrankHarrell
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