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I have a dataset of two patient and one healthy control group which I would like to compare (using R) with respect to a continuous outcome variable (each subjects is measured once). However the groups differ in age. In the context of the dataset the age effect is not of interest. My questions are:

  • What is the best way to account for the potentially confouding effect of age?

  • From what I know one could do this by either adding it as a covariate in the linear model or matching the samples with respect to age via usage of the propensity score. Which method is recommended in which context?

  • Is there an function to select subsamples of each group while matching groups with respect to age?

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Michael Posner wrote an interesting paper comparing propensity score matching with regression adjustment and a third technique using instrumental variables. The propensity score matching approach worked impressively in his examples. I will try to get you a link to the paper.

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