Biostatistical Modeling Concepts to Master
- 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
- Dummy variables and how their corresponding regression
coefficients are interpreted
- Interpretation of interaction effects
- Assumptions of interaction tests
- Writing null hypotheses precisely in terms of parameters being
- 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
- How knots are chosen
- How the number of knots relates to the flexibility allowed for the fit
- Which if any terms of a predictor that is expanded into multiple
constructed variables can be tested singly
- What tests of effects, interactions, and nonlinearity are powered
- Nonparametric smoothers
- Problems with naive approaches of handing missing data
- 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
- Understand regression to the mean
- Have an initial understanding of data reduction
- Elements of bootstrapping
- Model validation approaches and which methods of validation are
- How to display a complex regression model to a
- How to make a complex nonlinear relationship a non-issue to the
- A principle for estimating unknown parameters when least squares
is not appropriate.
- What is a Wald statistic and a likelihood ratio statistic in
general terms, and which one works better.
- When chi-square statistics are used instead of t or F
statistics, and how to approximately relate a chi-square statistic to an
- Exact interpretation of logistic model coefficients in the linear
- Assumptions of binary logistic regression.
- The value and use of a nonparametric smoother in examining
logistic model assumptions or in determining shapes of relationships
when Y is binary.
- How to convert between probabilities, odds, and log odds.
- Measures of predictive accuracy and predictive ability for binary
- What is meant by an ordinal response variable and what is assumed
about the data when you use a model or a rank test on an ordinal
- How to interpret coefficients in proportional odds models.
- What about odds ratios is assumed by the proportional odds
- How are ordinary nonparametric rank tests relate to the
proportional odds model.
- What is the value of only using the ordering of $Y$.
Last Modified: 10 Apr 2001