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**This is an old revision of the document!**
Table of Contents
Inference for Categorical Variables
effect modification vs confounding
if we don't take effect modification into account, we get an over-generalized estimate of teh realtionship between the outcome and the exposure for the entire co-hort
Cochran-Mantel_haenszel test
- limitations
- can only adjust for one variable at a time
looks at two binary categorical variables while adjusting for the value of a third categorical variable
Parametric One-Sample Inference of Categorical Variables
- one-sample proportion test
- do NOT use Yate's continuity, so specify:
- prop.test(…, correct = FALSE)
- $\Chi^2$ goodness of fit test
- to ensure sufficient sample size: $n \cdotp_{0} > 5$
- don't use continunity corrections!
- chisq.test(…, correct = FALSE)
NOTE: one-sample single proportion test gives a 95% CI – $\Chi^2$ does not!
Types of Probabilites
Joint, Marginal and Conditional Probabilities
- QI
- Stepped-wedge trial
- Linear regression
- plot of residuals
- these are different!
- correlative
- descriptive
- predictive
- associative
- confounding vs. effect modification
to assess a paired difference
- create histogram
- plot as box plot
- make Q-Q plot
duke_notes.1763412701.txt.gz · Last modified: by admin
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