one-sample t-test with correlated (clustered) observations
To handle the correlations, you can treat individuals as random blocks. So you have a mixed model with measurement technique crossed with measured attribute and random intercepts for each individual. You can fit this with lmer() in the lme4 package. Keep in mind there are a number of variations on this... like whether or not to include a measurement*attribute interaction, etc. good luck, ian
Paul Artes wrote:
I would like to estimate the difference between two measurement
techniques. With both techniques, 4 measurements were obtained in each of
15 individuals. (These are not *repeated* measurements though - each of
the 4 is of a different attribute). The naive approach would be a paired
t-test, but of course this assumes that the 4 measures contributed by each
individual are not dependent (which they are), and would inflate the CI of
the differences.
I found t.test.cluster {Hmisc}, but this works for the 2-sample problem
only as far as I understand...
Could someone please point me in the right direction?
Many thanks!
Paul
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