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Basic regression model question.

Suppose I treated 3 groups of mice, group 1 with water, group 2 with drug A, and group 3 with drug B. The water treatment thus serves as baseline.

Now my question is what is a more appropriate linear model to use in order to assess the effect of each drug relative to the water treatment:

  1. Do 2 pairwise comparisons: A vs. water and B vs. water?

  2. Use a linear model: response ~ treatment, where the levels in treatment are: water, A, and B with dummy coding.

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I'd sort of use a mix of the two options you have suggested. I'd fit the linear model response ~ treatment where treatment is at three levels. (note that if you are working in R then dummy coding is automated). Since you have a specific hypothesis in mind, I would also set a priori contrasts on the linear model to test whether water = a and water = b. Again, if you are working in R then setting contrasts like this is fairly straightforward.

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  • $\begingroup$ So I guess by running a linear model as response ~ treatment in R, it is actually doing all pairwise contrasts vs. the baseline level, correct? $\endgroup$ – dan Mar 25 '17 at 4:21
  • $\begingroup$ Yep, R uses the constraint of reference level set to zero. As long as water is your reference level then it will test the others against that $\endgroup$ – Conor Neilson Mar 25 '17 at 4:33

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