- also posted to sci.stat.consult, where the same question showed up.
Steve tells how to make the best of the data, making the likely
assumptions about the 120 days -
On 13 Oct 2000 10:38:51 -0700, [EMAIL PROTECTED] (Simon, Steve, PhD)
wrote:
> 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.
- Here are a couple of less-likely possibilities.
You don't say where the 120 days exist, so it might be that the are
paycheck cycles of 7, 14, 28 days, or a month; or menstrual cycles, or
some other. If the subjects have some overlapping '120 days' on the
calendar, it might be reasonable to look at calendar-date for cycles,
or for extreme events. That's assuming, there is a bit of day-to-day
lability that might cover up some information.
But if you aren't looking at (say) muggings on the day after Social
Security Checks appear, then I doubt that cycles are likely. Still,
the detail does allow you to exam on-set variations, or off-set --
That is, there might be a definite curve over the first week or so
that does not exist later, if the ratings are something that entail
learning or adaption. - This could be something interesting if it
varies among the three groups, or it could be something to be
eradicated because it is artifact.
On the other hand, if the 120-days was known to be a limit, there
might be some 'anticipation of the end' -- for instance, patients in
hospitals may show remarkable recovery during the last week of the
insurance coverage.
So, you can probably lump data by weeks or months, but don't forget to
take a look at start- and end-effects. If the measures have those
hazards.
--
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html
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