Hans-Christian Waldmann writes:
>Now, what am I supposed to do with data from a design giving a T=120
>time series for _each_ of 120 subjects ? There has been a controlled
>study where patients in three independent groups were asked to keep
>a diary on some outcome variables for ca. 4 months. There are some
>design variables like treat/control or sex and age that are expected
>to contribute systematically to variation between outcome measures.
>But this outcome measure apparently is a time series. I don't think
>I should perform an ANOVA-style analysis with a 120-level time factor.
>Pooling data and performing ARIMA/transfer-functions on a single time
>series of subjects' means for each point in time doesn't make sense
>either, assuming that subjects differ in both measurement level and
>covariance structure of their individual time series. I admit that
>I have no idea how to evaluate, say, an effect of treatment on this
>kind of outcome measure.
Even though the researchers collected data on 120 consecutive days, I doubt
that they are particularly interested in any one day in isolation. Look at
some composite measures, such as the slope of the trend line, or the change
score at the end of each month. Or perhaps an average for each month, or the
standard deviation for each month.
Your researchers should be able to elaborate on why they collected the data,
and that elaboration should help you decide which composite measure you
should use.
Once you reduce it to a small number of composite measures, then you can
apply the ANOVA types of procedures.
An alternative that might be worth exploring is fitting a spline model to
each subject's data and then pooling the splines across groups. This is
messy and complex, but fun.
I hope this helps. Good luck!
Steve Simon, [EMAIL PROTECTED], Standard Disclaimer.
STATS: STeve's Attempt to Teach Statistics. http://www.cmh.edu/stats
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