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What is the ‘correct’ way to compare the effect of a drug in different conditions? For example, if I have conditions a, b, c and d, where there are different numbers of ‘control’ and ‘drug’ cells in each condition and a single parameter has been measured in control and drug-treated cell (different cells).

Of course I can easily calculate the % effect of the drug for each condition, and determine whether the effect is significant (eg unpaired Student’s t-test or non-parametric equivalent). However, how do I appropriately determine whether the effect of the drug is different in each condition?

Comparing p-values across groups is not valid (e.g. Gelman, A., & Stern, H. (2006). The Difference Between “Significant” and ‘Not Significant’ is not Itself Statistically Significant. The American Statistician, 60(4), 328–331 or Nieuwenhuis, S., Forstmann, B. U., & Wagenmakers, E.-J. (2011). Erroneous analyses of interactions in neuroscience: a problem of significance. Nature Neuroscience, 14(9), 1105–1107).

Thanks

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Are conditions different diseases or symptoms (outcomes), or are they explanatory variables that interact with treatment (like smoking would be in a study of alcohol consumption and cirrhosis)? – Dimitriy V. Masterov Apr 16 '12 at 18:27
This question is a little hard to follow. Can you spell out a little more thoroughly what you are measuring and the design of the study? – gung Apr 16 '12 at 19:05
Sorry for the lack of clarity. 'Conditions' (a, b, c, d) are different subtypes of a protein expressed in heterologous cells. One electrophysiological measurement is made form each of several individual cells in each group. In other cells, expressing the same four receptor subtypes, the measurements are made after exposure to a drug - so there are groups: a(no drug), a(drug), b(no drug), b(drug) etc. Each of these groups is made up of a number of different cells. – user441706 Apr 17 '12 at 7:36

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