R help - Feb 2009 - one-sample t-test with correlated (clustered) observations

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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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
> 
-- 
View this message in context:
http://www.nabble.com/one-sample-t-test-with-correlated-%28clustered%29-observations-tp21875193p21876376.html
Sent from the R help mailing list archive at Nabble.com.