# Nested repeated measures analysis

I have a nested repeated measures question. I have 20 individuals grouped into 5 families. For each individual I have measurements on subsequent days pre, during, and post-treatment for a total of 6 measurements per individual (on days 1 and 2 pre-treatment, on days 4 and 5 during treatment, and days 7 and 8 post-treatment).

I previously used a model with composited pre, during and post-treatment level that looked like this:

lme(log(X)~t1+t2, random=~1|family/participant, data=data)


where t1 = -1 for pre-treatment and t2 = 1 for post-treatment

This allowed me to interpret the slopes on t1 and t2 to evaluate the treatment effect.

How do I incorporate the repeated measures within pre-treatment, during and post-treatment? Visualizing the data suggests correlation between these measurements on subsequent days and there may be a trend (slight up-tick).

Any suggestions on coding in R would be much appreciated!

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You say that you have 8 measurements per individual, but list 6 measurement occasions, can you clarify this? –  gung Jan 10 '13 at 20:52
Sorry, I have 6 measurements per individual: 2 pre, 2 during, 2 post –  Robin Jan 14 '13 at 21:50