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Regression models, such as linear, logistic, time to event, and mixed models, measure the strength of the association between the dependent variable and the independent variables.
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When you’re working with many correlated variables, they get too unwieldy to use individually.
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Cross-over trials provide a very powerful approach for comparing two treatment conditions. Research subjects get both treatment conditions, which we will label arbitrarily as A and B.
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Repeated measures ANOVA doesn’t cut it for many repeated measures situations, but do you always need mixed models instead?

Splines provide a useful way to model relationships that are more complex than a simple linear function. They work with a variety of regression models.

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